Clinical data hydra results (primarily using Hydra_k3)
Erica Baller
3/16/2018
R Markdown
This script goes through demographics, cnb scores, health, and psych summaries, adds clustering information, runs statistics and makes graphs from results.
Part 1 : Read in csv.s -This script reads in demographics, clinical scores, health and psych summaries, merges them, removes NAs, codes and separates by depression.
Part 2 : merge with hydra -It then merges these documents with hydra output (made in cbica), adding Hydra_k1 through Hydra_k10 columns (which represent the number of clusters) -The script reads in 3 different types of groups (matched, unmatched, and residualized unmatched groups), and also does all gender together as well as separating them by gender.
Part 3 : LM -The script then runs LM on each clinical score (clinical_measure ~ hydra_group).
-There is a test option that does this for all clinical measures and all hydra groups, but for the remainder of the analysis, Hydra_k3 was the only classification more deeply explored.
Part 4 : Anova -Anovas were also run on the results of the LM of each clinical value by cluster.
Part 5 : Graphing - Graphs were then made.
For continuous variables(age, medu1), the graphs represent means, with SEM as error bars For categorical variables (race, sex) the graphs are percentages (caucasian, male) per group, with chisq used to calculate significance
Part 6 : FDR Correction -FDR correction was calculated for each clinical measure ANOVA output -A table of the results was extracted
Part 7 : Demographics tables - Demographics tables for each group (matched, unmatched, resid) were produced
#######################################################
############ READ IN, MERGE AND SUBSET DATA############
#######################################################
#read in csvs
#######################################################
############ READ IN, MERGE AND SUBSET DATA############
#######################################################
#read in csvs
demographics <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/n9498_demographics_go1_20161212.csv", header = TRUE, sep = ",") #from /data/joy/BBL/projects/ballerDepHeterogen/data/n9498_demographics_go1_20161212.csv
clinical_scores <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/n9498_goassess_itemwise_bifactor_scores_20161219.csv", header = TRUE, sep = ",") #from /data/joy/BBL/projects/ballerDepHeterogen/data/n9498_goassess_itemwise_bifactor_scores_20161219.csv
health <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/n9498_health_20170405.csv", header = TRUE, sep = ",") #from /data/joy/BBL/projects/ballerDepHeterogen/data/n9498_health_20170405.csv
psych_summary <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/n9498_goassess_psych_summary_vars_20131014.csv", header = TRUE, sep = ",") #from /data/joy/BBL/projects/ballerDepHeterogen/data/n9498_goassess_psych_summary_vars_20131014.csv
#read in Hydra cluster data -> AG (all_gender), M(males), F(females)
hydra_AG_matched_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_all_gender_matched_n1424_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_AG_unmatched_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_all_gender_unmatched_n3022_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_AG_residuals_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_all_gender_unmatched_resid_n3022_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_F_matched_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_females_matched_n712_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_F_unmatched_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_females_unmatched_n1588_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_F_residuals_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_females_unmatched_resid_n1588_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_M_matched_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_males_matched_n712_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_M_unmatched_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_males_unmatched_n1434_HydraSubtypes.csv", header = TRUE, sep = ",")
hydra_M_residuals_clusters <- read.csv("/Users/eballer/BBL/from_chead/ballerDepHeterogen/hydra_output_from_cbica/CogData_males_unmatched_resid_n1434_HydraSubtypes.csv", header = TRUE, sep = ",")
#Coerce hydra values into factor format
hydra_names <- names(hydra_AG_matched_clusters[2:11])
hydra_AG_matched_clusters[hydra_names] <- lapply(hydra_AG_matched_clusters[hydra_names], factor)
hydra_AG_unmatched_clusters[hydra_names] <- lapply(hydra_AG_unmatched_clusters[hydra_names], factor)
hydra_AG_residuals_clusters[hydra_names] <- lapply(hydra_AG_residuals_clusters[hydra_names], factor)
hydra_F_matched_clusters[hydra_names] <- lapply(hydra_F_matched_clusters[hydra_names], factor)
hydra_F_unmatched_clusters[hydra_names] <- lapply(hydra_F_unmatched_clusters[hydra_names], factor)
hydra_F_residuals_clusters[hydra_names] <- lapply(hydra_F_residuals_clusters[hydra_names], factor)
hydra_M_matched_clusters[hydra_names] <- lapply(hydra_M_matched_clusters[hydra_names], factor)
hydra_M_unmatched_clusters[hydra_names] <- lapply(hydra_M_unmatched_clusters[hydra_names], factor)
hydra_M_residuals_clusters[hydra_names] <- lapply(hydra_M_residuals_clusters[hydra_names], factor)
############
#prepping and merging big clinical and demographics stuff
############
#remove people with NA for race, age, or sex. START WITH N = 9498
demographics_noNA_race <- demographics[!is.na(demographics$race),] #everyone has a race, N = 9498
demographics_noNA_race_age <- demographics_noNA_race[!is.na(demographics_noNA_race$ageAtClinicalAssess1),] # 86 people do not have age at clinical assessment. N = 9412
demographics_noNA_race_age_sex <- demographics_noNA_race_age[!is.na(demographics_noNA_race_age$sex),] #everyone has a sex, N = 9412
#remove people with NA for depression or total psych score, START WITH N = 9498
psych_summary_no_NA_dep <- psych_summary[!is.na(psych_summary$smry_dep),] #take out those with NA for depression, 87 people N = 9411
psych_summary_no_NA_dep_and_smry_psych_overall <- psych_summary_no_NA_dep[!is.na(psych_summary_no_NA_dep$smry_psych_overall_rtg),] #take out those with NA for overall psych rtg, no additional people lost, N = 9411
#merge demographics and clinical #this is if we want to include people without full demographic data
dem_clinical <- merge(demographics_noNA_race_age_sex, clinical_scores, by = "bblid") #merge demographics and clinical, N = 9350
psych_health <- merge(psych_summary_no_NA_dep_and_smry_psych_overall, health, by = "bblid") #merge psych and health, N = 9411
dem_clinical_psych_health_merged <- merge (dem_clinical, psych_health, by = "bblid") #merge all 3 csvs, lost 1 person [134716] (had demographics, but no psych ratings): N = 9350
#make subsets
subset_just_dep_and_no_medicalratingExclude <- subset.data.frame(dem_clinical_psych_health_merged, (medicalratingExclude == 0) & (smry_dep == 4), select = "bblid") #subset people who were not medically excluded and who are depressed, N = 776
subset_no_psych_no_medicalratingExclude <- subset.data.frame(dem_clinical_psych_health_merged, (medicalratingExclude == 0) & (smry_psych_overall_rtg < 4), select = "bblid") #subset people who are psychiatrically healthy, N = 2508
subset_dep_or_no_psych_and_no_medicalratingExclude <- subset.data.frame(dem_clinical_psych_health_merged, (medicalratingExclude == 0) & ((smry_dep == 4) | (smry_psych_overall_rtg <4))) #subset including both depressed and healthies, good for regressions, N = 3284
#would binarize depression smry score to -1 (less than 4, not depressed) and 1 (score 4 , depressed)
dep_binarized <- ifelse(subset_dep_or_no_psych_and_no_medicalratingExclude$smry_dep == 4, 1, -1)
subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED <- cbind(subset_dep_or_no_psych_and_no_medicalratingExclude, dep_binarized) #N = 3284
#make depression and gender into factor scores
subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$dep_binarized <- as.factor(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$dep_binarized)
subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$sex <- as.factor(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$sex)
#divide ageAtclinical by 12 for age
subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$age_in_years <- subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$ageAtClinicalAssess1/12
#race binarized for plotting purposes, caucasian 1, non-caucasion 0
subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$race_binarized <- ifelse(as.factor(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED$race) == 1, 1, 0)
#remove people with NA in their cognitive measures
subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED <- subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED[complete.cases(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED[14:39]),]
################################################
### Merge clustering data with subsetted data ##
################################################
#AG (all gender), M (males), F (females)
#AG Matched - n 1423
#AG Unmatched n = 3014
#AG Resid n = 3014
subset_with_clusters_AG_matched <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_AG_matched_clusters, by = "bblid")
subset_with_clusters_AG_unmatched <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_AG_unmatched_clusters, by = "bblid")
subset_with_clusters_AG_resid <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_AG_residuals_clusters, by = "bblid")
#F Matched - n 952
#F Unmatched n = 1584
#F Resid n = 1585
subset_with_clusters_F_matched <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_F_matched_clusters, by = "bblid")
subset_with_clusters_F_unmatched <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_F_unmatched_clusters, by = "bblid")
subset_with_clusters_F_resid <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_F_residuals_clusters, by = "bblid")
#M Matched - n 470
#M Unmatched n = 1430
#M Resid n = 1430
subset_with_clusters_M_matched <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_M_matched_clusters, by = "bblid")
subset_with_clusters_M_unmatched <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_M_unmatched_clusters, by = "bblid")
subset_with_clusters_M_resid <- merge(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED, hydra_M_residuals_clusters, by = "bblid")
#save objects
saveRDS(object = subset_with_clusters_AG_matched, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_AG_matched_clinical.rds")
saveRDS(object = subset_with_clusters_AG_unmatched, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_AG_unmatched_clinical.rds")
saveRDS(object = subset_with_clusters_AG_resid, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_AG_resid_clinical.rds")
saveRDS(object = subset_with_clusters_F_matched, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_F_matched_clinical.rds")
saveRDS(object = subset_with_clusters_F_unmatched, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_F_unmatched_clinical.rds")
saveRDS(object = subset_with_clusters_F_resid, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_F_resid_clinical.rds")
saveRDS(object = subset_with_clusters_M_matched, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_M_matched_clinical.rds")
saveRDS(object = subset_with_clusters_M_unmatched, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_M_unmatched_clinical.rds")
saveRDS(object = subset_with_clusters_M_resid, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/data/subset_with_clusters_M_resid_clinical.rds")Including Graphs
#################################
# Linear Model for each measure #
##### Results stored in list ####
#################################
#get clinical measure names (grep factor)
clinical_measure_names <- names(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED)[grep("factor", names(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED))] #get the names of all the columns with _z in the name
cluster_names <- colnames(hydra_AG_matched_clusters[,2:11])
clinical_measure_names_list <- names(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED)[grep("factor", names(subset_dep_or_no_psych_and_no_medicalratingExclude_DEPBINARIZED))] #get the names of all the columns with _z in the name
cluster_names_list <- colnames(hydra_AG_matched_clusters[,2:11])
#### All Hydra clusters in embedded list######
clinical_cog_score_cluster_stats_lm_AG_matched_by_cluster_1through10 <- lapply(cluster_names_list, function(cluster_name)
{
clinical_cog_score_cluster_stats_lm_AG_matched_withincluster<- lapply(clinical_measure_names_list, cluster=as.name(cluster_name), function(clinical_measure_name, cluster)
{
clinical_measure <- as.name(clinical_measure_name)
lm(substitute(clinical_measure ~ cluster, list(clinical_measure = clinical_measure, cluster = cluster)), data = subset_with_clusters_AG_matched)
})
setNames(clinical_cog_score_cluster_stats_lm_AG_matched_withincluster, clinical_measure_names_list)
})
names(clinical_cog_score_cluster_stats_lm_AG_matched_by_cluster_1through10) <- cluster_names_list
##### Just Hydra_3 clusters ######
clinical_cog_score_cluster_stats_lm_AG_matched <- lapply(clinical_measure_names, function(x)
{
lm(substitute(i ~ Hydra_k3, list(i = as.name(x))), data = subset_with_clusters_AG_matched)
})
clinical_cog_score_cluster_stats_lm_AG_unmatched <- lapply(clinical_measure_names, function(x)
{
lm(substitute(i ~ Hydra_k3, list(i = as.name(x))), data = subset_with_clusters_AG_unmatched)
})
clinical_cog_score_cluster_stats_lm_AG_resid <- lapply(clinical_measure_names, function(x)
{
lm(substitute(i ~ Hydra_k3, list(i = as.name(x))), data = subset_with_clusters_AG_resid)
})
##############################
####### Statistics ###########
##############################
######## All hydra clusters, Anova results ########
clinical_cog_score_cluster_stats_anova_AG_matched_by_cluster_1through10 <- lapply(cluster_names_list, function(cluster_name)
{
clinical_cog_score_cluster_stats_anova_AG_withincluster <- lapply(clinical_measure_names_list, cluster=as.name(cluster_name), function(clinical_measure_name, cluster)
{
clinical_measure <- as.name(clinical_measure_name)
anova(lm(substitute(clinical_measure ~ cluster, list(clinical_measure = clinical_measure, cluster = cluster)), data = subset_with_clusters_AG_matched))
})
setNames(clinical_cog_score_cluster_stats_anova_AG_withincluster, clinical_measure_names_list)
})
names(clinical_cog_score_cluster_stats_anova_AG_matched_by_cluster_1through10) <- cluster_names_list
#####Just Hydra 3 clusters ANOVA ########
clinical_cog_score_cluster_stats_anova_AG_matched <- lapply(clinical_measure_names, function(x)
{
anova(lm(substitute(i ~ Hydra_k3, list(i = as.name(x))), data = subset_with_clusters_AG_matched))
})
clinical_cog_score_cluster_stats_anova_AG_unmatched <- lapply(clinical_measure_names, function(x)
{
anova(lm(substitute(i ~ Hydra_k3, list(i = as.name(x))), data = subset_with_clusters_AG_unmatched))
})
clinical_cog_score_cluster_stats_anova_AG_resid <- lapply(clinical_measure_names, function(x)
{
anova(lm(substitute(i ~ Hydra_k3, list(i = as.name(x))), data = subset_with_clusters_AG_resid))
})
names(clinical_cog_score_cluster_stats_lm_AG_matched) <- clinical_measure_names
names(clinical_cog_score_cluster_stats_lm_AG_unmatched) <- clinical_measure_names
names(clinical_cog_score_cluster_stats_lm_AG_resid) <- clinical_measure_names
names(clinical_cog_score_cluster_stats_anova_AG_matched) <- clinical_measure_names
names(clinical_cog_score_cluster_stats_anova_AG_unmatched) <- clinical_measure_names
names(clinical_cog_score_cluster_stats_anova_AG_resid) <- clinical_measure_names
#checkmodel with visreg, uncomment when want to check
lapply(clinical_cog_score_cluster_stats_lm_AG_matched, function(x) {visreg(x)}) 
## $mood_4factorv2
## $fit
## Hydra_k3 mood_4factorv2 visregFit visregLwr visregUpr
## 1 -1 0.369047 -0.1752193 -0.2438189 -0.1066197
## 2 1 0.369047 0.8819416 0.7689345 0.9949487
## 3 2 0.369047 1.1616092 1.0486021 1.2746163
## 4 3 0.369047 1.0372187 0.9038120 1.1706253
##
## $res
## Hydra_k3 mood_4factorv2 visregRes visregPos
## 1 -1 0.369047 1.467683543 TRUE
## 2 1 0.369047 0.357056433 FALSE
## 3 3 0.369047 2.573814188 TRUE
## 4 -1 0.369047 0.820192434 TRUE
## 5 1 0.369047 0.394526983 FALSE
## 6 -1 0.369047 -1.288650136 FALSE
## 7 -1 0.369047 0.255136536 TRUE
## 8 -1 0.369047 0.503940990 TRUE
## 9 -1 0.369047 0.547406828 TRUE
## 10 2 0.369047 0.144223707 FALSE
## 11 -1 0.369047 2.979994953 TRUE
## 12 -1 0.369047 2.979994953 TRUE
## 13 -1 0.369047 -0.897457591 FALSE
## 14 -1 0.369047 -1.194224349 FALSE
## 15 -1 0.369047 0.277618866 TRUE
## 16 2 0.369047 2.636764712 TRUE
## 17 -1 0.369047 -0.653149603 FALSE
## 18 3 0.369047 2.941025580 TRUE
## 19 -1 0.369047 -0.599192010 FALSE
## 20 1 0.369047 0.536915074 FALSE
## 21 -1 0.369047 2.950018512 TRUE
## 22 2 0.369047 0.178696613 FALSE
## 23 -1 0.369047 -0.534742664 FALSE
## 24 -1 0.369047 0.899630001 TRUE
## 25 -1 0.369047 1.359768358 TRUE
## 26 -1 0.369047 -1.513473437 FALSE
## 27 -1 0.369047 -1.526962835 FALSE
## 28 -1 0.369047 0.431997533 TRUE
## 29 -1 0.369047 0.724267826 TRUE
## 30 -1 0.369047 2.845100972 TRUE
## 31 -1 0.369047 0.791714816 TRUE
## 32 -1 0.369047 0.195183656 TRUE
## 33 -1 0.369047 -0.585702612 FALSE
## 34 -1 0.369047 -0.952914005 FALSE
## 35 -1 0.369047 -0.345891090 FALSE
## 36 -1 0.369047 -0.669636645 FALSE
## 37 -1 0.369047 0.854665341 TRUE
## 38 -1 0.369047 0.794712460 TRUE
## 39 -1 0.369047 -0.993382199 FALSE
## 40 3 0.369047 1.981779493 TRUE
## 41 -1 0.369047 1.139441523 TRUE
## 42 -1 0.369047 1.993770069 TRUE
## 43 -1 0.369047 -0.855490574 FALSE
## 44 -1 0.369047 0.773728952 TRUE
## 45 -1 0.369047 -1.389071210 FALSE
## 46 -1 0.369047 0.917615865 TRUE
## 47 1 0.369047 0.530919786 FALSE
## 48 3 0.369047 1.400236553 TRUE
## 49 -1 0.369047 -1.191226705 FALSE
## 50 -1 0.369047 -0.317413472 FALSE
## 51 -1 0.369047 1.942810121 TRUE
## 52 3 0.369047 1.849883156 TRUE
## 53 -1 0.369047 2.226087481 TRUE
## 54 1 0.369047 1.975784205 TRUE
## 55 -1 0.369047 -0.104580747 TRUE
## 56 -1 0.369047 0.695790207 TRUE
## 57 1 0.369047 1.541125822 TRUE
## 58 3 0.369047 1.530634068 TRUE
## 59 -1 0.369047 -0.028140824 TRUE
## 60 -1 0.369047 -0.552728528 FALSE
## 61 -1 0.369047 -0.620175519 FALSE
## 62 -1 0.369047 2.686225838 TRUE
## 63 2 0.369047 2.845100972 TRUE
## 64 2 0.369047 0.390030517 FALSE
## 65 -1 0.369047 0.760239554 TRUE
## 66 -1 0.369047 -0.031138468 TRUE
## 67 -1 0.369047 1.188902649 TRUE
## 68 -1 0.369047 0.348063501 TRUE
## 69 -1 0.369047 -0.651650781 FALSE
## 70 -1 0.369047 0.313590594 TRUE
## 71 2 0.369047 0.662816123 FALSE
## 72 -1 0.369047 -0.927434031 FALSE
## 73 -1 0.369047 -0.182519491 FALSE
## 74 -1 0.369047 0.782721884 TRUE
## 75 2 0.369047 2.013254755 TRUE
## 76 2 0.369047 0.190687190 FALSE
## 77 -1 0.369047 1.735972683 TRUE
## 78 2 0.369047 1.466184721 TRUE
## 79 1 0.369047 2.035737085 TRUE
## 80 3 0.369047 -0.954412827 FALSE
## 81 -1 0.369047 -0.780549474 FALSE
## 82 -1 0.369047 1.274335504 TRUE
## 83 -1 0.369047 1.680516269 TRUE
## 84 -1 0.369047 0.166706037 TRUE
## 85 1 0.369047 1.569603440 TRUE
## 86 -1 0.369047 -1.209212569 FALSE
## 87 -1 0.369047 -0.922937565 FALSE
## 88 -1 0.369047 -0.279942922 FALSE
## 89 1 0.369047 1.245857886 TRUE
## 90 -1 0.369047 -0.094088993 TRUE
## 91 -1 0.369047 0.319585883 TRUE
## 92 1 0.369047 1.818407894 TRUE
## 93 -1 0.369047 -0.097086637 TRUE
## 94 -1 0.369047 1.807916140 TRUE
## 95 -1 0.369047 -0.909448167 FALSE
## 96 3 0.369047 0.719771360 FALSE
## 97 -1 0.369047 -1.057831546 FALSE
## 98 -1 0.369047 0.297103552 TRUE
## 99 2 0.369047 3.618493129 TRUE
## 100 -1 0.369047 -1.434035871 FALSE
## 101 -1 0.369047 -0.410340437 FALSE
## 102 -1 0.369047 -1.963120041 FALSE
## 103 3 0.369047 0.022819124 FALSE
## 104 3 0.369047 2.008758289 TRUE
## 105 -1 0.369047 -0.100084281 TRUE
## 106 2 0.369047 1.184406183 TRUE
## 107 -1 0.369047 -1.111789138 FALSE
## 108 -1 0.369047 -1.086309164 FALSE
## 109 -1 0.369047 -0.500269758 FALSE
## 110 -1 0.369047 -0.656147247 FALSE
## 111 3 0.369047 0.360054077 FALSE
## 112 3 0.369047 1.694005667 TRUE
## 113 -1 0.369047 0.569889158 TRUE
## 114 -1 0.369047 0.929606441 TRUE
## 115 1 0.369047 1.071994532 TRUE
## 116 -1 0.369047 -0.335399336 FALSE
## 117 2 0.369047 0.970074635 FALSE
## 118 -1 0.369047 0.554900938 TRUE
## 119 -1 0.369047 -2.418761932 FALSE
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## 1070 1 0.369047 0.039306166 FALSE
## 1071 -1 0.369047 -0.692118975 FALSE
## 1072 1 0.369047 0.988060500 TRUE
## 1073 -1 0.369047 0.554900938 TRUE
## 1074 3 0.369047 1.206888513 TRUE
## 1075 -1 0.369047 0.189188368 TRUE
## 1076 2 0.369047 0.550404472 FALSE
## 1077 3 0.369047 0.629842039 FALSE
## 1078 -1 0.369047 0.316588239 TRUE
## 1079 2 0.369047 1.911334859 TRUE
## 1080 1 0.369047 -0.339895802 FALSE
## 1081 2 0.369047 0.112748445 FALSE
## 1082 -1 0.369047 -0.738582457 FALSE
## 1083 2 0.369047 1.151432099 FALSE
## 1084 1 0.369047 1.975784205 TRUE
## 1085 2 0.369047 -0.037133756 FALSE
## 1086 -1 0.369047 0.231155384 TRUE
## 1087 -1 0.369047 -0.044627866 TRUE
## 1088 -1 0.369047 -0.191512423 FALSE
## 1089 1 0.369047 0.000336794 FALSE
## 1090 2 0.369047 1.640048074 TRUE
## 1091 -1 0.369047 -0.272448812 FALSE
## 1092 3 0.369047 2.558825967 TRUE
## 1093 -1 0.369047 -0.722095415 FALSE
## 1094 -1 0.369047 0.868154739 TRUE
## 1095 -1 0.369047 -1.114786782 FALSE
## 1096 -1 0.369047 0.925109975 TRUE
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## 1098 1 0.369047 1.257848462 TRUE
## 1099 -1 0.369047 1.391243621 TRUE
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## 1103 -1 0.369047 -0.934928141 FALSE
## 1104 2 0.369047 2.052224128 TRUE
## 1105 1 0.369047 1.043516914 TRUE
## 1106 -1 0.369047 -0.573712036 FALSE
## 1107 3 0.369047 0.228157740 FALSE
## 1108 2 0.369047 -0.961906937 FALSE
## 1109 1 0.369047 0.556399760 FALSE
## 1110 -1 0.369047 0.060289675 TRUE
## 1111 -1 0.369047 -0.067110196 TRUE
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## 1113 1 0.369047 1.885854884 TRUE
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## 1116 -1 0.369047 -0.644156671 FALSE
## 1117 -1 0.369047 1.647542185 TRUE
## 1118 -1 0.369047 0.547406828 TRUE
## 1119 -1 0.369047 1.487168229 TRUE
## 1120 -1 0.369047 -1.227198433 FALSE
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## 1122 2 0.369047 0.899630001 FALSE
## 1123 -1 0.369047 0.977568745 TRUE
## 1124 1 0.369047 1.786932632 TRUE
## 1125 2 0.369047 1.379253045 TRUE
## 1126 1 0.369047 0.367548187 FALSE
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## 1128 -1 0.369047 -0.554227350 FALSE
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## 1131 -1 0.369047 0.908622933 TRUE
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## 1133 -1 0.369047 0.187689546 TRUE
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## 1135 -1 0.369047 -0.750573034 FALSE
## 1136 -1 0.369047 -1.120782070 FALSE
## 1137 -1 0.369047 -0.892961125 FALSE
## 1138 -1 0.369047 0.761738376 TRUE
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## 1141 -1 0.369047 0.118743733 TRUE
## 1142 1 0.369047 0.539912718 FALSE
## 1143 2 0.369047 1.950304231 TRUE
## 1144 -1 0.369047 -1.062328012 FALSE
## 1145 -1 0.369047 1.127450947 TRUE
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## 1164 -1 0.369047 0.991058144 TRUE
## 1165 -1 0.369047 0.980566390 TRUE
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## 1186 1 0.369047 1.965292451 TRUE
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## 1188 -1 0.369047 -1.032351572 FALSE
## 1189 1 0.369047 2.163136956 TRUE
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## 1192 -1 0.369047 -0.176524203 FALSE
## 1193 3 0.369047 2.157141668 TRUE
## 1194 -1 0.369047 1.867869020 TRUE
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## 1198 2 0.369047 1.412227129 TRUE
## 1199 2 0.369047 1.458690611 TRUE
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## 1203 -1 0.369047 -0.651650781 FALSE
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## 1207 -1 0.369047 0.638834971 TRUE
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## 1256 2 0.369047 -0.041630222 FALSE
## 1257 -1 0.369047 1.575598728 TRUE
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## 1261 -1 0.369047 0.446985753 TRUE
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## 1266 -1 0.369047 1.897845460 TRUE
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## 1270 -1 0.369047 0.220663630 TRUE
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## 1290 -1 0.369047 1.286326080 TRUE
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## 1309 2 0.369047 2.555828323 TRUE
## 1310 -1 0.369047 -1.327619508 FALSE
## 1311 -1 0.369047 -1.535955767 FALSE
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## 1316 -1 0.369047 -0.693617797 FALSE
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## 1318 2 0.369047 1.616066922 TRUE
## 1319 3 0.369047 0.321084705 FALSE
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## 1324 1 0.369047 1.101970972 TRUE
## 1325 1 0.369047 1.711991531 TRUE
## 1326 -1 0.369047 -0.636662561 FALSE
## 1327 2 0.369047 3.654464858 TRUE
## 1328 3 0.369047 1.433210637 TRUE
## 1329 -1 0.369047 -0.288935854 FALSE
## 1330 -1 0.369047 0.910121755 TRUE
## 1331 -1 0.369047 -0.879471727 FALSE
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## 1333 3 0.369047 0.854665341 FALSE
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## 1354 2 0.369047 1.683513913 TRUE
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## 1359 -1 0.369047 -0.251465304 FALSE
## 1360 -1 0.369047 -0.825514134 FALSE
## 1361 -1 0.369047 -0.898956413 FALSE
## 1362 -1 0.369047 0.535416252 TRUE
## 1363 -1 0.369047 -0.870478794 FALSE
## 1364 -1 0.369047 1.514147026 TRUE
## 1365 3 0.369047 0.052795565 FALSE
## 1366 3 0.369047 1.281829614 TRUE
## 1367 -1 0.369047 -0.728090703 FALSE
## 1368 -1 0.369047 -0.815022380 FALSE
## 1369 -1 0.369047 -0.957410471 FALSE
## 1370 -1 0.369047 0.255136536 TRUE
## 1371 3 0.369047 0.646329081 FALSE
## 1372 3 0.369047 1.301314300 TRUE
## 1373 -1 0.369047 0.781223062 TRUE
## 1374 -1 0.369047 -0.889963481 FALSE
## 1375 3 0.369047 1.888852528 TRUE
## 1376 -1 0.369047 -0.794038872 FALSE
## 1377 3 0.369047 2.876576234 TRUE
## 1378 -1 0.369047 -0.921438743 FALSE
## 1379 3 0.369047 1.021034584 FALSE
## 1380 3 0.369047 1.169417963 TRUE
## 1381 -1 0.369047 -0.876474082 FALSE
## 1382 -1 0.369047 -0.383361641 FALSE
## 1383 2 0.369047 2.843602150 TRUE
## 1384 -1 0.369047 -1.673847393 FALSE
## 1385 3 0.369047 1.371758934 TRUE
## 1386 1 0.369047 1.993770069 TRUE
## 1387 -1 0.369047 -1.159751443 FALSE
## 1388 3 0.369047 1.503655271 TRUE
## 1389 3 0.369047 1.535130534 TRUE
## 1390 -1 0.369047 -1.374082990 FALSE
## 1391 -1 0.369047 0.280616510 TRUE
## 1392 -1 0.369047 0.448484576 TRUE
## 1393 -1 0.369047 -0.270949990 FALSE
## 1394 1 0.369047 0.265628290 FALSE
## 1395 1 0.369047 0.093263759 FALSE
## 1396 -1 0.369047 -0.699613085 FALSE
## 1397 1 0.369047 0.613354997 FALSE
## 1398 -1 0.369047 -0.743078923 FALSE
## 1399 1 0.369047 1.131947413 TRUE
## 1400 -1 0.369047 -0.624671985 FALSE
## 1401 3 0.369047 1.424217705 TRUE
## 1402 -1 0.369047 1.359768358 TRUE
## 1403 -1 0.369047 -0.762563610 FALSE
## 1404 3 0.369047 1.458690611 TRUE
## 1405 2 0.369047 1.992271247 TRUE
## 1406 1 0.369047 -0.996379843 FALSE
## 1407 2 0.369047 2.322012090 TRUE
## 1408 3 0.369047 0.265628290 FALSE
## 1409 1 0.369047 0.992556966 TRUE
## 1410 1 0.369047 0.426002245 FALSE
## 1411 2 0.369047 1.322297808 TRUE
## 1412 1 0.369047 0.632839683 FALSE
## 1413 -1 0.369047 0.653823191 TRUE
## 1414 2 0.369047 1.788431454 TRUE
## 1415 3 0.369047 0.859161807 FALSE
## 1416 1 0.369047 1.647542185 TRUE
## 1417 -1 0.369047 0.046800276 TRUE
## 1418 3 0.369047 2.131661694 TRUE
## 1419 -1 0.369047 -0.178023025 FALSE
## 1420 -1 0.369047 -0.753570678 FALSE
## 1421 1 0.369047 0.322583527 FALSE
## 1422 -1 0.369047 -0.773055364 FALSE
## 1423 1 0.369047 0.162209571 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "mood_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "mood_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $psychosis_4factorv2
## $fit
## Hydra_k3 psychosis_4factorv2 visregFit visregLwr visregUpr
## 1 -1 -0.1889013 -0.05238930 -0.12692767 0.02214907
## 2 1 -0.1889013 0.15698237 0.03419207 0.27977267
## 3 2 -0.1889013 -0.13330944 -0.25609974 -0.01051914
## 4 3 -0.1889013 -0.06085942 -0.20581528 0.08409644
##
## $res
## Hydra_k3 psychosis_4factorv2 visregRes visregPos
## 1 -1 -0.1889013 1.765287093 TRUE
## 2 1 -0.1889013 0.784220987 TRUE
## 3 3 -0.1889013 -0.002220669 TRUE
## 4 -1 -0.1889013 -0.234578430 FALSE
## 5 1 -0.1889013 -0.953497318 FALSE
## 6 -1 -0.1889013 -2.375447382 FALSE
## 7 -1 -0.1889013 1.034452423 TRUE
## 8 -1 -0.1889013 -0.304087163 FALSE
## 9 -1 -0.1889013 -0.304087163 FALSE
## 10 2 -0.1889013 2.053251841 TRUE
## 11 -1 -0.1889013 0.371140521 TRUE
## 12 -1 -0.1889013 1.153610250 TRUE
## 13 -1 -0.1889013 -0.037968017 TRUE
## 14 -1 -0.1889013 -0.379553786 FALSE
## 15 -1 -0.1889013 1.457462708 TRUE
## 16 2 -0.1889013 -0.270325778 FALSE
## 17 -1 -0.1889013 0.919266524 TRUE
## 18 3 -0.1889013 1.743441491 TRUE
## 19 -1 -0.1889013 -0.157125843 FALSE
## 20 1 -0.1889013 -0.081659220 FALSE
## 21 -1 -0.1889013 -0.302101199 FALSE
## 22 2 -0.1889013 0.827912190 TRUE
## 23 -1 -0.1889013 -0.393455533 FALSE
## 24 -1 -0.1889013 -0.387497641 FALSE
## 25 -1 -0.1889013 -0.331890656 FALSE
## 26 -1 -0.1889013 0.728614001 TRUE
## 27 -1 -0.1889013 -0.983286774 FALSE
## 28 -1 -0.1889013 -0.192873191 FALSE
## 29 -1 -0.1889013 1.455476744 TRUE
## 30 -1 -0.1889013 -0.363666076 FALSE
## 31 -1 -0.1889013 -0.391469569 FALSE
## 32 -1 -0.1889013 -0.226634575 FALSE
## 33 -1 -0.1889013 -0.024066270 TRUE
## 34 -1 -0.1889013 1.225104946 TRUE
## 35 -1 -0.1889013 -0.254438068 FALSE
## 36 -1 -0.1889013 -0.335862583 FALSE
## 37 -1 -0.1889013 -0.163083735 FALSE
## 38 -1 -0.1889013 1.773230948 TRUE
## 39 -1 -0.1889013 -1.803489814 FALSE
## 40 3 -0.1889013 -0.361680112 FALSE
## 41 -1 -0.1889013 1.421715360 TRUE
## 42 -1 -0.1889013 -0.828381600 FALSE
## 43 -1 -0.1889013 -2.625678818 FALSE
## 44 -1 -0.1889013 0.923238451 TRUE
## 45 -1 -0.1889013 -1.160037551 FALSE
## 46 -1 -0.1889013 1.141694467 TRUE
## 47 1 -0.1889013 -1.305012906 FALSE
## 48 3 -0.1889013 -0.397427460 FALSE
## 49 -1 -0.1889013 -1.700219698 FALSE
## 50 -1 -0.1889013 1.322417171 TRUE
## 51 -1 -0.1889013 -0.280255597 FALSE
## 52 3 -0.1889013 0.553849189 TRUE
## 53 -1 -0.1889013 -0.385511678 FALSE
## 54 1 -0.1889013 0.790178878 TRUE
## 55 -1 -0.1889013 -0.087617111 FALSE
## 56 -1 -0.1889013 -0.278269634 FALSE
## 57 1 -0.1889013 -0.262381923 FALSE
## 58 3 -0.1889013 -0.427216917 FALSE
## 59 -1 -0.1889013 -0.107476749 FALSE
## 60 -1 -0.1889013 -0.349764330 FALSE
## 61 -1 -0.1889013 -0.212732829 FALSE
## 62 -1 -0.1889013 -0.407357279 FALSE
## 63 2 -0.1889013 -0.190887227 FALSE
## 64 2 -0.1889013 -1.791574032 FALSE
## 65 -1 -0.1889013 1.151624286 TRUE
## 66 -1 -0.1889013 -0.163083735 FALSE
## 67 -1 -0.1889013 -0.425230953 FALSE
## 68 -1 -0.1889013 -1.162023514 FALSE
## 69 -1 -0.1889013 -0.496725649 FALSE
## 70 -1 -0.1889013 -0.451048482 FALSE
## 71 2 -0.1889013 0.657119305 TRUE
## 72 -1 -0.1889013 -0.089603075 FALSE
## 73 -1 -0.1889013 -0.361680112 FALSE
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##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "psychosis_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "psychosis_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $externalizing_4factorv2
## $fit
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##
## $res
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## 1423 1 -0.2380737 1.764791063 TRUE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "externalizing_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "externalizing_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $phobias_4factorv2
## $fit
## Hydra_k3 phobias_4factorv2 visregFit visregLwr visregUpr
## 1 -1 -0.1084109 -0.23026751 -0.30414496 -0.15639005
## 2 1 -0.1084109 0.37357703 0.25187549 0.49527857
## 3 2 -0.1084109 -0.02517121 -0.14687276 0.09653033
## 4 3 -0.1084109 0.05105678 -0.09261378 0.19472735
##
## $res
## Hydra_k3 phobias_4factorv2 visregRes visregPos
## 1 -1 -0.1084109 1.099418669 TRUE
## 2 1 -0.1084109 0.290138024 FALSE
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## 15 -1 -0.1084109 1.833862610 TRUE
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## 1362 -1 -0.1084109 -1.267509670 FALSE
## 1363 -1 -0.1084109 -1.199634519 FALSE
## 1364 -1 -0.1084109 -1.130018980 FALSE
## 1365 3 -0.1084109 -0.003987630 FALSE
## 1366 3 -0.1084109 -1.481577454 FALSE
## 1367 -1 -0.1084109 -0.762797010 FALSE
## 1368 -1 -0.1084109 -0.936835858 FALSE
## 1369 -1 -0.1084109 0.387599779 TRUE
## 1370 -1 -0.1084109 -0.433863586 FALSE
## 1371 3 -0.1084109 0.598186786 TRUE
## 1372 3 -0.1084109 0.932341375 TRUE
## 1373 -1 -0.1084109 -0.590498550 FALSE
## 1374 -1 -0.1084109 -0.834152937 FALSE
## 1375 3 -0.1084109 -1.081288102 FALSE
## 1376 -1 -0.1084109 -0.959460908 FALSE
## 1377 3 -0.1084109 1.035024295 TRUE
## 1378 -1 -0.1084109 0.352792009 TRUE
## 1379 3 -0.1084109 0.960187590 TRUE
## 1380 3 -0.1084109 0.568600181 TRUE
## 1381 -1 -0.1084109 -0.461709802 FALSE
## 1382 -1 -0.1084109 0.599927174 TRUE
## 1383 2 -0.1084109 0.267512973 TRUE
## 1384 -1 -0.1084109 -0.037055011 TRUE
## 1385 3 -0.1084109 -1.746116504 FALSE
## 1386 1 -0.1084109 -0.989047513 FALSE
## 1387 -1 -0.1084109 0.988033806 TRUE
## 1388 3 -0.1084109 0.880129720 TRUE
## 1389 3 -0.1084109 0.834879619 TRUE
## 1390 -1 -0.1084109 -1.389336864 FALSE
## 1391 -1 -0.1084109 -0.660114089 FALSE
## 1392 -1 -0.1084109 -1.408481138 FALSE
## 1393 -1 -0.1084109 -0.331180665 FALSE
## 1394 1 -0.1084109 0.330166959 FALSE
## 1395 1 -0.1084109 0.486801923 TRUE
## 1396 -1 -0.1084109 -0.303334450 FALSE
## 1397 1 -0.1084109 -0.221536191 FALSE
## 1398 -1 -0.1084109 -0.602681269 FALSE
## 1399 1 -0.1084109 1.727698912 TRUE
## 1400 -1 -0.1084109 -0.886364592 FALSE
## 1401 3 -0.1084109 1.586727445 TRUE
## 1402 -1 -0.1084109 1.363957719 TRUE
## 1403 -1 -0.1084109 0.754821749 TRUE
## 1404 3 -0.1084109 0.027339363 FALSE
## 1405 2 -0.1084109 -0.037055011 FALSE
## 1406 1 -0.1084109 2.013122624 TRUE
## 1407 2 -0.1084109 4.340022028 TRUE
## 1408 3 -0.1084109 1.282159460 TRUE
## 1409 1 -0.1084109 1.132486050 TRUE
## 1410 1 -0.1084109 1.196880424 TRUE
## 1411 2 -0.1084109 0.464176872 TRUE
## 1412 1 -0.1084109 2.937268909 TRUE
## 1413 -1 -0.1084109 1.701593085 TRUE
## 1414 2 -0.1084109 1.078534007 TRUE
## 1415 3 -0.1084109 0.981072252 TRUE
## 1416 1 -0.1084109 1.880853099 TRUE
## 1417 -1 -0.1084109 -1.027336059 FALSE
## 1418 3 -0.1084109 1.602390941 TRUE
## 1419 -1 -0.1084109 -0.964682074 FALSE
## 1420 -1 -0.1084109 -0.780200894 FALSE
## 1421 1 -0.1084109 -1.845318647 FALSE
## 1422 -1 -0.1084109 -0.144959097 TRUE
## 1423 1 -0.1084109 1.353515388 TRUE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "phobias_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "phobias_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $overall_psychopathology_4factorv2
## $fit
## Hydra_k3 overall_psychopathology_4factorv2 visregFit visregLwr
## 1 -1 0.2343075 -0.4891738 -0.5472916
## 2 1 0.2343075 0.9977547 0.9020148
## 3 2 0.2343075 0.8504538 0.7547139
## 4 3 0.2343075 0.9532282 0.8402057
## visregUpr
## 1 -0.4310560
## 2 1.0934947
## 3 0.9461938
## 4 1.0662507
##
## $res
## Hydra_k3 overall_psychopathology_4factorv2 visregRes visregPos
## 1 -1 0.2343075 1.450718839 TRUE
## 2 1 0.2343075 0.816178515 FALSE
## 3 3 0.2343075 0.882642225 FALSE
## 4 -1 0.2343075 -0.252256972 TRUE
## 5 1 0.2343075 1.571105936 TRUE
## 6 -1 0.2343075 0.792351902 TRUE
## 7 -1 0.2343075 0.221767223 TRUE
## 8 -1 0.2343075 -0.042833584 TRUE
## 9 -1 0.2343075 -0.168236810 TRUE
## 10 2 0.2343075 1.016823677 TRUE
## 11 -1 0.2343075 0.743444644 TRUE
## 12 -1 0.2343075 0.898944644 TRUE
## 13 -1 0.2343075 -1.225386007 FALSE
## 14 -1 0.2343075 -0.357595682 TRUE
## 15 -1 0.2343075 -0.001450520 TRUE
## 16 2 0.2343075 -0.291131972 FALSE
## 17 -1 0.2343075 -0.799015038 FALSE
## 18 3 0.2343075 0.931549483 FALSE
## 19 -1 0.2343075 -0.708724715 FALSE
## 20 1 0.2343075 0.778557547 FALSE
## 21 -1 0.2343075 0.121444642 TRUE
## 22 2 0.2343075 1.522198678 TRUE
## 23 -1 0.2343075 0.022376093 TRUE
## 24 -1 0.2343075 0.254372062 TRUE
## 25 -1 0.2343075 -0.662325522 FALSE
## 26 -1 0.2343075 -0.267305359 TRUE
## 27 -1 0.2343075 -0.158204552 TRUE
## 28 -1 0.2343075 -0.851684393 FALSE
## 29 -1 0.2343075 -0.046595681 TRUE
## 30 -1 0.2343075 0.093855932 TRUE
## 31 -1 0.2343075 -0.548208586 FALSE
## 32 -1 0.2343075 -0.521873908 FALSE
## 33 -1 0.2343075 -1.948962622 FALSE
## 34 -1 0.2343075 -0.382676327 TRUE
## 35 -1 0.2343075 -0.728789231 FALSE
## 36 -1 0.2343075 -0.666087618 FALSE
## 37 -1 0.2343075 -1.017216652 FALSE
## 38 -1 0.2343075 0.328359965 TRUE
## 39 -1 0.2343075 -0.128107778 TRUE
## 40 3 0.2343075 -0.498047295 FALSE
## 41 -1 0.2343075 2.149214809 TRUE
## 42 -1 0.2343075 0.463795449 TRUE
## 43 -1 0.2343075 1.678952711 TRUE
## 44 -1 0.2343075 -0.041579552 TRUE
## 45 -1 0.2343075 -0.348817456 TRUE
## 46 -1 0.2343075 0.189162384 TRUE
## 47 1 0.2343075 2.160501099 TRUE
## 48 3 0.2343075 -0.032801326 FALSE
## 49 -1 0.2343075 0.954122064 TRUE
## 50 -1 0.2343075 1.148497064 TRUE
## 51 -1 0.2343075 0.096363997 TRUE
## 52 3 0.2343075 1.611234969 TRUE
## 53 -1 0.2343075 0.443730933 TRUE
## 54 1 0.2343075 0.290738997 FALSE
## 55 -1 0.2343075 -1.032265039 FALSE
## 56 -1 0.2343075 0.049964803 TRUE
## 57 1 0.2343075 -0.410265037 FALSE
## 58 3 0.2343075 0.276944642 FALSE
## 59 -1 0.2343075 0.149033352 TRUE
## 60 -1 0.2343075 -0.916894071 FALSE
## 61 -1 0.2343075 -0.931942458 FALSE
## 62 -1 0.2343075 -0.519365844 FALSE
## 63 2 0.2343075 0.039932545 FALSE
## 64 2 0.2343075 1.297726903 TRUE
## 65 -1 0.2343075 0.319581739 TRUE
## 66 -1 0.2343075 -0.583321489 FALSE
## 67 -1 0.2343075 0.250609965 TRUE
## 68 -1 0.2343075 0.308295449 TRUE
## 69 -1 0.2343075 -0.587083586 FALSE
## 70 -1 0.2343075 0.165335771 TRUE
## 71 2 0.2343075 0.804892225 FALSE
## 72 -1 0.2343075 -0.905607780 FALSE
## 73 -1 0.2343075 -0.732551328 FALSE
## 74 -1 0.2343075 1.312775290 TRUE
## 75 2 0.2343075 1.330331742 TRUE
## 76 2 0.2343075 1.275154323 TRUE
## 77 -1 0.2343075 0.250609965 TRUE
## 78 2 0.2343075 0.046202706 FALSE
## 79 1 0.2343075 0.228037384 FALSE
## 80 3 0.2343075 2.026319647 TRUE
## 81 -1 0.2343075 -1.377123911 FALSE
## 82 -1 0.2343075 -0.429075521 TRUE
## 83 -1 0.2343075 0.791097870 TRUE
## 84 -1 0.2343075 -0.121837617 TRUE
## 85 1 0.2343075 0.146525287 FALSE
## 86 -1 0.2343075 -0.416535198 TRUE
## 87 -1 0.2343075 -0.205857778 TRUE
## 88 -1 0.2343075 0.189162384 TRUE
## 89 1 0.2343075 0.807400289 FALSE
## 90 -1 0.2343075 -0.796506974 FALSE
## 91 -1 0.2343075 -0.114313423 TRUE
## 92 1 0.2343075 -0.331261004 FALSE
## 93 -1 0.2343075 0.557847869 TRUE
## 94 -1 0.2343075 -0.244732778 TRUE
## 95 -1 0.2343075 -1.260498910 FALSE
## 96 3 0.2343075 0.718363998 FALSE
## 97 -1 0.2343075 -0.920656167 FALSE
## 98 -1 0.2343075 -0.424059392 TRUE
## 99 2 0.2343075 -0.358849714 FALSE
## 100 -1 0.2343075 0.902706741 TRUE
## 101 -1 0.2343075 0.304533352 TRUE
## 102 -1 0.2343075 0.856307547 TRUE
## 103 3 0.2343075 0.744698676 FALSE
## 104 3 0.2343075 2.225710777 TRUE
## 105 -1 0.2343075 -1.767127944 FALSE
## 106 2 0.2343075 0.688267224 FALSE
## 107 -1 0.2343075 -0.963293264 FALSE
## 108 -1 0.2343075 -0.351325521 TRUE
## 109 -1 0.2343075 0.732158353 TRUE
## 110 -1 0.2343075 0.214243029 TRUE
## 111 3 0.2343075 0.551577708 FALSE
## 112 3 0.2343075 -0.089232778 FALSE
## 113 -1 0.2343075 -0.378914230 TRUE
## 114 -1 0.2343075 -0.372644069 TRUE
## 115 1 0.2343075 1.864549486 TRUE
## 116 -1 0.2343075 -0.663579554 FALSE
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## 119 -1 0.2343075 1.479561581 TRUE
## 120 2 0.2343075 0.581674482 FALSE
## 121 1 0.2343075 1.198658355 TRUE
## 122 -1 0.2343075 -0.761394070 FALSE
## 123 3 0.2343075 1.673936582 TRUE
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## 125 -1 0.2343075 -0.589591650 FALSE
## 126 -1 0.2343075 0.883896257 TRUE
## 127 -1 0.2343075 0.649392224 TRUE
## 128 -1 0.2343075 -0.521873908 FALSE
## 129 -1 0.2343075 0.597976901 TRUE
## 130 2 0.2343075 -0.283607779 FALSE
## 131 2 0.2343075 1.341618032 TRUE
## 132 -1 0.2343075 -0.860462619 FALSE
## 133 -1 0.2343075 0.423666417 TRUE
## 134 -1 0.2343075 -1.285579556 FALSE
## 135 -1 0.2343075 -1.324454556 FALSE
## 136 -1 0.2343075 -1.340756975 FALSE
## 137 -1 0.2343075 0.228037384 TRUE
## 138 -1 0.2343075 0.490130127 TRUE
## 139 1 0.2343075 1.796831743 TRUE
## 140 -1 0.2343075 1.820658356 TRUE
## 141 2 0.2343075 0.945343838 TRUE
## 142 1 0.2343075 0.128968836 FALSE
## 143 3 0.2343075 1.015569644 TRUE
## 144 2 0.2343075 1.078271258 TRUE
## 145 1 0.2343075 1.302743032 TRUE
## 146 2 0.2343075 0.733412386 FALSE
## 147 -1 0.2343075 -0.386438424 TRUE
## 148 3 0.2343075 0.372251094 FALSE
## 149 -1 0.2343075 -0.391454553 TRUE
## 150 2 0.2343075 1.201166419 TRUE
## 151 -1 0.2343075 -0.232192456 TRUE
## 152 -1 0.2343075 -0.628466650 FALSE
## 153 -1 0.2343075 -1.567736814 FALSE
## 154 -1 0.2343075 -0.150680359 TRUE
## 155 -1 0.2343075 -1.261752943 FALSE
## 156 -1 0.2343075 0.260642223 TRUE
## 157 -1 0.2343075 -0.906861813 FALSE
## 158 2 0.2343075 0.171605932 FALSE
## 159 -1 0.2343075 -1.102490845 FALSE
## 160 -1 0.2343075 -1.538894072 FALSE
## 161 2 0.2343075 0.940327709 TRUE
## 162 2 0.2343075 -0.174506972 FALSE
## 163 -1 0.2343075 -0.215890036 TRUE
## 164 -1 0.2343075 -1.051075523 FALSE
## 165 -1 0.2343075 0.541545450 TRUE
## 166 -1 0.2343075 -0.829111812 FALSE
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## 168 2 0.2343075 1.503388194 TRUE
## 169 2 0.2343075 0.674472869 FALSE
## 170 -1 0.2343075 -0.790236812 FALSE
## 171 3 0.2343075 -0.010228746 FALSE
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## 173 -1 0.2343075 -0.699946489 FALSE
## 174 -1 0.2343075 0.971678515 TRUE
## 175 -1 0.2343075 -0.666087618 FALSE
## 176 -1 0.2343075 -1.572752943 FALSE
## 177 -1 0.2343075 -0.005212616 TRUE
## 178 -1 0.2343075 1.598694646 TRUE
## 179 3 0.2343075 1.464513194 TRUE
## 180 -1 0.2343075 -0.200841649 TRUE
## 181 -1 0.2343075 0.703315611 TRUE
## 182 1 0.2343075 0.817432547 FALSE
## 183 3 0.2343075 1.598694646 TRUE
## 184 -1 0.2343075 -0.348817456 TRUE
## 185 3 0.2343075 2.175549486 TRUE
## 186 3 0.2343075 2.392497068 TRUE
## 187 -1 0.2343075 -0.824095683 FALSE
## 188 -1 0.2343075 0.470065611 TRUE
## 189 1 0.2343075 1.609980936 TRUE
## 190 2 0.2343075 0.309549481 FALSE
## 191 -1 0.2343075 0.318327707 TRUE
## 192 -1 0.2343075 0.218005126 TRUE
## 193 -1 0.2343075 -0.392708585 TRUE
## 194 1 0.2343075 0.421158352 FALSE
## 195 -1 0.2343075 -1.156414233 FALSE
## 196 -1 0.2343075 -0.896829554 FALSE
## 197 1 0.2343075 -0.163220681 FALSE
## 198 2 0.2343075 1.256343839 TRUE
## 199 2 0.2343075 -0.328752940 FALSE
## 200 -1 0.2343075 -0.347563424 TRUE
## 201 -1 0.2343075 -1.689377944 FALSE
## 202 -1 0.2343075 -1.357059394 FALSE
## 203 -1 0.2343075 -0.642261005 FALSE
## 204 -1 0.2343075 0.024884158 TRUE
## 205 -1 0.2343075 1.016823677 TRUE
## 206 3 0.2343075 1.472037388 TRUE
## 207 2 0.2343075 1.604964807 TRUE
## 208 -1 0.2343075 -1.362075523 FALSE
## 209 3 0.2343075 1.222484968 TRUE
## 210 3 0.2343075 1.033126096 TRUE
## 211 3 0.2343075 1.315283355 TRUE
## 212 1 0.2343075 0.827464805 FALSE
## 213 3 0.2343075 0.319581739 FALSE
## 214 -1 0.2343075 -0.228430359 TRUE
## 215 2 0.2343075 -0.130615843 FALSE
## 216 -1 0.2343075 -1.834845686 FALSE
## 217 -1 0.2343075 -0.634736812 FALSE
## 218 1 0.2343075 2.356130132 TRUE
## 219 -1 0.2343075 0.939073677 TRUE
## 220 1 0.2343075 1.083287387 TRUE
## 221 2 0.2343075 -0.055373907 FALSE
## 222 -1 0.2343075 0.211734965 TRUE
## 223 2 0.2343075 0.915247064 TRUE
## 224 -1 0.2343075 0.808654321 TRUE
## 225 2 0.2343075 1.087049483 TRUE
## 226 3 0.2343075 1.292710774 TRUE
## 227 -1 0.2343075 0.911484967 TRUE
## 228 -1 0.2343075 0.280706739 TRUE
## 229 2 0.2343075 2.198122067 TRUE
## 230 3 0.2343075 0.067521255 FALSE
## 231 3 0.2343075 0.511448675 FALSE
## 232 1 0.2343075 1.568597872 TRUE
## 233 -1 0.2343075 0.523988998 TRUE
## 234 -1 0.2343075 -0.875511006 FALSE
## 235 2 0.2343075 1.166053516 TRUE
## 236 2 0.2343075 1.068238999 TRUE
## 237 1 0.2343075 0.481351901 FALSE
## 238 -1 0.2343075 -0.067914230 TRUE
## 239 -1 0.2343075 0.609263192 TRUE
## 240 -1 0.2343075 -0.332515037 TRUE
## 241 -1 0.2343075 0.991743031 TRUE
## 242 -1 0.2343075 -1.773398105 FALSE
## 243 -1 0.2343075 0.666948676 TRUE
## 244 -1 0.2343075 1.340364000 TRUE
## 245 2 0.2343075 1.189880129 TRUE
## 246 2 0.2343075 0.684505127 FALSE
## 247 1 0.2343075 1.327823678 TRUE
## 248 -1 0.2343075 0.182892223 TRUE
## 249 2 0.2343075 0.619295450 FALSE
## 250 2 0.2343075 0.737174482 FALSE
## 251 2 0.2343075 -0.144410197 FALSE
## 252 2 0.2343075 0.304533352 FALSE
## 253 -1 0.2343075 -0.765156167 FALSE
## 254 2 0.2343075 1.628791420 TRUE
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## 260 -1 0.2343075 -0.393962618 TRUE
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## 262 3 0.2343075 -0.067914230 FALSE
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## 264 -1 0.2343075 0.298263191 TRUE
## 265 -1 0.2343075 -1.506289234 FALSE
## 266 3 0.2343075 1.004283354 TRUE
## 267 1 0.2343075 1.233771258 TRUE
## 268 -1 0.2343075 -1.374615846 FALSE
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## 270 -1 0.2343075 0.828718837 TRUE
## 271 3 0.2343075 1.340364000 TRUE
## 272 -1 0.2343075 0.626819643 TRUE
## 273 -1 0.2343075 0.054980932 TRUE
## 274 -1 0.2343075 -0.269813424 TRUE
## 275 -1 0.2343075 -1.709442460 FALSE
## 276 1 0.2343075 0.223021255 FALSE
## 277 -1 0.2343075 0.679488998 TRUE
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## 280 1 0.2343075 1.635061582 TRUE
## 281 -1 0.2343075 -0.299910198 TRUE
## 282 -1 0.2343075 -1.210337620 FALSE
## 283 3 0.2343075 0.845021257 FALSE
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## 285 1 0.2343075 2.664622068 TRUE
## 286 -1 0.2343075 -1.290595685 FALSE
## 287 -1 0.2343075 -0.324990843 TRUE
## 288 -1 0.2343075 -1.565228750 FALSE
## 289 2 0.2343075 1.972396260 TRUE
## 290 3 0.2343075 2.046384163 TRUE
## 291 -1 0.2343075 -0.027785197 TRUE
## 292 1 0.2343075 1.058206741 TRUE
## 293 2 0.2343075 1.795577711 TRUE
## 294 2 0.2343075 0.834988999 FALSE
## 295 3 0.2343075 1.754194647 TRUE
## 296 3 0.2343075 1.661396259 TRUE
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## 300 -1 0.2343075 -1.393426330 FALSE
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## 303 -1 0.2343075 -0.679881973 FALSE
## 304 -1 0.2343075 -0.011482778 TRUE
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## 1249 -1 0.2343075 -1.340756975 FALSE
## 1250 1 0.2343075 1.530976904 TRUE
## 1251 -1 0.2343075 -0.319974714 TRUE
## 1252 -1 0.2343075 -0.120583585 TRUE
## 1253 -1 0.2343075 -0.056627939 TRUE
## 1254 2 0.2343075 0.743444644 FALSE
## 1255 1 0.2343075 2.669638197 TRUE
## 1256 2 0.2343075 1.786799485 TRUE
## 1257 -1 0.2343075 -0.855446490 FALSE
## 1258 -1 0.2343075 -0.824095683 FALSE
## 1259 -1 0.2343075 -1.034773103 FALSE
## 1260 2 0.2343075 0.540291417 FALSE
## 1261 -1 0.2343075 0.011089803 TRUE
## 1262 -1 0.2343075 0.737174482 TRUE
## 1263 1 0.2343075 -0.392708585 FALSE
## 1264 3 0.2343075 1.061968838 TRUE
## 1265 -1 0.2343075 -1.229148104 FALSE
## 1266 -1 0.2343075 -0.967055361 FALSE
## 1267 1 0.2343075 1.396795452 TRUE
## 1268 -1 0.2343075 -1.214099717 FALSE
## 1269 -1 0.2343075 -0.179523101 TRUE
## 1270 -1 0.2343075 -1.434809395 FALSE
## 1271 -1 0.2343075 -0.233446488 TRUE
## 1272 -1 0.2343075 -0.523127940 FALSE
## 1273 3 0.2343075 -0.861716651 FALSE
## 1274 1 0.2343075 1.651364001 TRUE
## 1275 1 0.2343075 0.096363997 FALSE
## 1276 1 0.2343075 -0.239716649 FALSE
## 1277 -1 0.2343075 -0.531906166 FALSE
## 1278 -1 0.2343075 -0.720011006 FALSE
## 1279 3 0.2343075 0.798622063 FALSE
## 1280 -1 0.2343075 -1.033519071 FALSE
## 1281 2 0.2343075 1.233771258 TRUE
## 1282 -1 0.2343075 -0.806539232 FALSE
## 1283 3 0.2343075 2.458960778 TRUE
## 1284 -1 0.2343075 -1.306898104 FALSE
## 1285 2 0.2343075 -0.144410197 FALSE
## 1286 3 0.2343075 0.840005128 FALSE
## 1287 -1 0.2343075 -0.420297295 TRUE
## 1288 -1 0.2343075 0.338392223 TRUE
## 1289 -1 0.2343075 -0.846668264 FALSE
## 1290 -1 0.2343075 -0.989627942 FALSE
## 1291 -1 0.2343075 -1.893785202 FALSE
## 1292 -1 0.2343075 -0.247240843 TRUE
## 1293 -1 0.2343075 -1.952724719 FALSE
## 1294 1 0.2343075 0.184146255 FALSE
## 1295 -1 0.2343075 -0.929434393 FALSE
## 1296 -1 0.2343075 -1.940184396 FALSE
## 1297 -1 0.2343075 -0.494285198 FALSE
## 1298 -1 0.2343075 -0.103027133 TRUE
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## 1300 -1 0.2343075 -0.538176328 FALSE
## 1301 2 0.2343075 -0.436599714 FALSE
## 1302 -1 0.2343075 1.257597871 TRUE
## 1303 -1 0.2343075 -0.218398101 TRUE
## 1304 3 0.2343075 -0.076692455 FALSE
## 1305 -1 0.2343075 0.631835772 TRUE
## 1306 2 0.2343075 0.727142224 FALSE
## 1307 2 0.2343075 -0.269813424 FALSE
## 1308 -1 0.2343075 -2.037998912 FALSE
## 1309 2 0.2343075 1.119654322 TRUE
## 1310 -1 0.2343075 -0.330006972 TRUE
## 1311 -1 0.2343075 -0.006466649 TRUE
## 1312 1 0.2343075 1.591170453 TRUE
## 1313 1 0.2343075 -0.341293263 FALSE
## 1314 1 0.2343075 1.478307549 TRUE
## 1315 3 0.2343075 0.194178513 FALSE
## 1316 -1 0.2343075 -1.950216654 FALSE
## 1317 -1 0.2343075 -1.190273104 FALSE
## 1318 2 0.2343075 0.350932546 FALSE
## 1319 3 0.2343075 1.691493033 TRUE
## 1320 1 0.2343075 0.811162386 FALSE
## 1321 1 0.2343075 1.579884162 TRUE
## 1322 -1 0.2343075 -0.242224714 TRUE
## 1323 -1 0.2343075 -1.103744878 FALSE
## 1324 1 0.2343075 1.415605936 TRUE
## 1325 1 0.2343075 -0.268559391 FALSE
## 1326 -1 0.2343075 -2.002886009 FALSE
## 1327 2 0.2343075 -0.192063423 FALSE
## 1328 3 0.2343075 0.133984965 FALSE
## 1329 -1 0.2343075 -0.431583585 TRUE
## 1330 -1 0.2343075 -0.619688425 FALSE
## 1331 -1 0.2343075 -1.619152137 FALSE
## 1332 -1 0.2343075 -0.579559392 FALSE
## 1333 3 0.2343075 1.720335775 TRUE
## 1334 -1 0.2343075 -1.827321493 FALSE
## 1335 1 0.2343075 2.264585777 TRUE
## 1336 -1 0.2343075 -1.310660201 FALSE
## 1337 1 0.2343075 1.344126097 TRUE
## 1338 1 0.2343075 -0.237208585 FALSE
## 1339 -1 0.2343075 0.397331740 TRUE
## 1340 1 0.2343075 1.949823679 TRUE
## 1341 -1 0.2343075 -1.649248911 FALSE
## 1342 3 0.2343075 0.165335771 FALSE
## 1343 -1 0.2343075 -1.170208587 FALSE
## 1344 -1 0.2343075 -0.116821488 TRUE
## 1345 -1 0.2343075 -1.216607781 FALSE
## 1346 -1 0.2343075 -0.545700521 FALSE
## 1347 2 0.2343075 1.104605935 TRUE
## 1348 1 0.2343075 0.337138191 FALSE
## 1349 1 0.2343075 1.148497064 TRUE
## 1350 1 0.2343075 0.833734967 FALSE
## 1351 2 0.2343075 0.432444643 FALSE
## 1352 -1 0.2343075 -0.687406167 FALSE
## 1353 3 0.2343075 1.468275291 TRUE
## 1354 2 0.2343075 -0.282353746 FALSE
## 1355 1 0.2343075 2.159247067 TRUE
## 1356 -1 0.2343075 -0.967055361 FALSE
## 1357 -1 0.2343075 -0.047849713 TRUE
## 1358 -1 0.2343075 -0.296148101 TRUE
## 1359 -1 0.2343075 0.101380126 TRUE
## 1360 -1 0.2343075 -1.698156170 FALSE
## 1361 -1 0.2343075 -1.482462621 FALSE
## 1362 -1 0.2343075 -0.370136005 TRUE
## 1363 -1 0.2343075 -1.145127942 FALSE
## 1364 -1 0.2343075 -0.316212617 TRUE
## 1365 3 0.2343075 1.597440614 TRUE
## 1366 3 0.2343075 0.705823676 FALSE
## 1367 -1 0.2343075 -1.946454557 FALSE
## 1368 -1 0.2343075 -1.733269073 FALSE
## 1369 -1 0.2343075 1.251327710 TRUE
## 1370 -1 0.2343075 -1.094966652 FALSE
## 1371 3 0.2343075 1.720335775 TRUE
## 1372 3 0.2343075 1.118400290 TRUE
## 1373 -1 0.2343075 -0.129361810 TRUE
## 1374 -1 0.2343075 -1.681853750 FALSE
## 1375 3 0.2343075 0.532767224 FALSE
## 1376 -1 0.2343075 -1.457381975 FALSE
## 1377 3 0.2343075 0.280706739 FALSE
## 1378 -1 0.2343075 -1.178986813 FALSE
## 1379 3 0.2343075 1.198658355 TRUE
## 1380 3 0.2343075 1.238787387 TRUE
## 1381 -1 0.2343075 -1.338248911 FALSE
## 1382 -1 0.2343075 -0.615926328 FALSE
## 1383 2 0.2343075 0.393569643 FALSE
## 1384 -1 0.2343075 0.036170448 TRUE
## 1385 3 0.2343075 0.360964804 FALSE
## 1386 1 0.2343075 0.088839803 FALSE
## 1387 -1 0.2343075 -0.646023102 FALSE
## 1388 3 0.2343075 1.528468839 TRUE
## 1389 3 0.2343075 0.281960771 FALSE
## 1390 -1 0.2343075 -0.277337617 TRUE
## 1391 -1 0.2343075 -1.113777136 FALSE
## 1392 -1 0.2343075 0.472573675 TRUE
## 1393 -1 0.2343075 -0.158204552 TRUE
## 1394 1 0.2343075 1.209944645 TRUE
## 1395 1 0.2343075 1.487085775 TRUE
## 1396 -1 0.2343075 -0.941974716 FALSE
## 1397 1 0.2343075 2.110339809 TRUE
## 1398 -1 0.2343075 0.986726902 TRUE
## 1399 1 0.2343075 1.441940613 TRUE
## 1400 -1 0.2343075 -1.928898106 FALSE
## 1401 3 0.2343075 1.836960776 TRUE
## 1402 -1 0.2343075 -0.629720683 FALSE
## 1403 -1 0.2343075 0.388553514 TRUE
## 1404 3 0.2343075 0.829972870 FALSE
## 1405 2 0.2343075 -0.213381972 FALSE
## 1406 1 0.2343075 1.591170453 TRUE
## 1407 2 0.2343075 0.190416416 FALSE
## 1408 3 0.2343075 0.620549482 FALSE
## 1409 1 0.2343075 0.735920450 FALSE
## 1410 1 0.2343075 0.748460773 FALSE
## 1411 2 0.2343075 0.962900289 TRUE
## 1412 1 0.2343075 0.732158353 FALSE
## 1413 -1 0.2343075 0.164081739 TRUE
## 1414 2 0.2343075 0.394823675 FALSE
## 1415 3 0.2343075 0.249355933 FALSE
## 1416 1 0.2343075 0.605501095 FALSE
## 1417 -1 0.2343075 0.266912384 TRUE
## 1418 3 0.2343075 0.329613997 FALSE
## 1419 -1 0.2343075 -1.357059394 FALSE
## 1420 -1 0.2343075 -1.841115847 FALSE
## 1421 1 0.2343075 2.214424487 TRUE
## 1422 -1 0.2343075 -1.701918266 FALSE
## 1423 1 0.2343075 0.537783353 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "overall_psychopathology_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "overall_psychopathology_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"lapply(clinical_cog_score_cluster_stats_lm_AG_unmatched, function(x) {visreg(x)}) 
## $mood_4factorv2
## $fit
## Hydra_k3 mood_4factorv2 visregFit visregLwr visregUpr
## 1 -1 -0.1240654 -0.2685618 -0.3043495 -0.2327741
## 2 1 -0.1240654 0.8897926 0.7850202 0.9945649
## 3 2 -0.1240654 1.0472036 0.9291245 1.1652826
## 4 3 -0.1240654 1.1510731 1.0398934 1.2622529
##
## $res
## Hydra_k3 mood_4factorv2 visregRes visregPos
## 1 -1 -0.1240654 0.409515203 TRUE
## 2 -1 -0.1240654 0.358555255 TRUE
## 3 -1 -0.1240654 1.467683543 TRUE
## 4 -1 -0.1240654 -0.839003532 FALSE
## 5 -1 -0.1240654 -0.121067789 TRUE
## 6 -1 -0.1240654 0.433496355 TRUE
## 7 -1 -0.1240654 0.436493999 TRUE
## 8 -1 -0.1240654 -0.653149603 FALSE
## 9 1 -0.1240654 0.357056433 FALSE
## 10 -1 -0.1240654 1.152930921 TRUE
## 11 3 -0.1240654 2.573814188 TRUE
## 12 -1 -0.1240654 0.820192434 TRUE
## 13 1 -0.1240654 0.394526983 FALSE
## 14 -1 -0.1240654 -1.101297384 FALSE
## 15 -1 -0.1240654 -1.012866886 FALSE
## 16 -1 -0.1240654 -1.288650136 FALSE
## 17 -1 -0.1240654 0.255136536 TRUE
## 18 -1 -0.1240654 1.088481574 TRUE
## 19 -1 -0.1240654 -1.481998175 FALSE
## 20 -1 -0.1240654 0.503940990 TRUE
## 21 -1 -0.1240654 1.403234197 TRUE
## 22 -1 -0.1240654 0.695790207 TRUE
## 23 -1 -0.1240654 0.547406828 TRUE
## 24 -1 -0.1240654 0.985062856 TRUE
## 25 1 -0.1240654 0.144223707 FALSE
## 26 -1 -0.1240654 1.064500422 TRUE
## 27 -1 -0.1240654 2.979994953 TRUE
## 28 -1 -0.1240654 1.741967971 TRUE
## 29 -1 -0.1240654 0.351061145 TRUE
## 30 -1 -0.1240654 -0.877972905 FALSE
## 31 -1 -0.1240654 2.979994953 TRUE
## 32 -1 -0.1240654 -0.897457591 FALSE
## 33 -1 -0.1240654 -1.194224349 FALSE
## 34 -1 -0.1240654 0.307595306 TRUE
## 35 -1 -0.1240654 2.326508556 TRUE
## 36 -1 -0.1240654 -1.520967547 FALSE
## 37 -1 -0.1240654 0.277618866 TRUE
## 38 2 -0.1240654 2.636764712 TRUE
## 39 -1 -0.1240654 0.724267826 TRUE
## 40 -1 -0.1240654 -0.653149603 FALSE
## 41 -1 -0.1240654 1.163422675 TRUE
## 42 2 -0.1240654 2.941025580 TRUE
## 43 -1 -0.1240654 -0.599192010 FALSE
## 44 -1 -0.1240654 0.195183656 TRUE
## 45 -1 -0.1240654 1.121455659 TRUE
## 46 -1 -0.1240654 -0.408841615 FALSE
## 47 1 -0.1240654 0.536915074 FALSE
## 48 -1 -0.1240654 0.931105263 TRUE
## 49 -1 -0.1240654 2.950018512 TRUE
## 50 -1 -0.1240654 -0.997878665 FALSE
## 51 -1 -0.1240654 0.118743733 TRUE
## 52 3 -0.1240654 0.178696613 FALSE
## 53 -1 -0.1240654 -0.534742664 FALSE
## 54 -1 -0.1240654 0.899630001 TRUE
## 55 -1 -0.1240654 1.187403827 TRUE
## 56 -1 -0.1240654 1.359768358 TRUE
## 57 -1 -0.1240654 -1.513473437 FALSE
## 58 -1 -0.1240654 -1.526962835 FALSE
## 59 -1 -0.1240654 -1.526962835 FALSE
## 60 -1 -0.1240654 1.830398470 TRUE
## 61 -1 -0.1240654 0.431997533 TRUE
## 62 -1 -0.1240654 -0.966403403 FALSE
## 63 -1 -0.1240654 0.724267826 TRUE
## 64 -1 -0.1240654 0.605860887 TRUE
## 65 -1 -0.1240654 2.845100972 TRUE
## 66 -1 -0.1240654 0.791714816 TRUE
## 67 -1 -0.1240654 0.195183656 TRUE
## 68 -1 -0.1240654 -0.585702612 FALSE
## 69 -1 -0.1240654 -0.952914005 FALSE
## 70 -1 -0.1240654 -0.345891090 FALSE
## 71 -1 -0.1240654 1.392742443 TRUE
## 72 -1 -0.1240654 -1.090805630 FALSE
## 73 -1 -0.1240654 -0.669636645 FALSE
## 74 -1 -0.1240654 0.854665341 TRUE
## 75 -1 -0.1240654 -0.818020024 FALSE
## 76 -1 -0.1240654 0.707780783 TRUE
## 77 -1 -0.1240654 0.794712460 TRUE
## 78 -1 -0.1240654 -0.993382199 FALSE
## 79 2 -0.1240654 1.981779493 TRUE
## 80 -1 -0.1240654 1.139441523 TRUE
## 81 -1 -0.1240654 1.993770069 TRUE
## 82 -1 -0.1240654 -0.855490574 FALSE
## 83 -1 -0.1240654 0.773728952 TRUE
## 84 -1 -0.1240654 -0.209498287 TRUE
## 85 -1 -0.1240654 -1.389071210 FALSE
## 86 -1 -0.1240654 0.917615865 TRUE
## 87 1 -0.1240654 0.530919786 FALSE
## 88 -1 -0.1240654 1.661031583 TRUE
## 89 2 -0.1240654 1.400236553 TRUE
## 90 -1 -0.1240654 0.309094128 TRUE
## 91 -1 -0.1240654 -1.191226705 FALSE
## 92 -1 -0.1240654 -0.317413472 FALSE
## 93 -1 -0.1240654 1.942810121 TRUE
## 94 -1 -0.1240654 0.688296097 TRUE
## 95 2 -0.1240654 1.849883156 TRUE
## 96 -1 -0.1240654 1.042018092 TRUE
## 97 -1 -0.1240654 0.925109975 TRUE
## 98 -1 -0.1240654 2.226087481 TRUE
## 99 -1 -0.1240654 -1.435534693 FALSE
## 100 1 -0.1240654 1.975784205 TRUE
## 101 -1 -0.1240654 -0.104580747 TRUE
## 102 -1 -0.1240654 0.791714816 TRUE
## 103 -1 -0.1240654 0.695790207 TRUE
## 104 1 -0.1240654 1.541125822 TRUE
## 105 2 -0.1240654 1.530634068 TRUE
## 106 -1 -0.1240654 -0.028140824 TRUE
## 107 -1 -0.1240654 0.312091772 TRUE
## 108 -1 -0.1240654 0.692792563 TRUE
## 109 -1 -0.1240654 0.718272538 TRUE
## 110 -1 -0.1240654 -0.552728528 FALSE
## 111 -1 -0.1240654 -0.620175519 FALSE
## 112 -1 -0.1240654 2.686225838 TRUE
## 113 3 -0.1240654 2.845100972 TRUE
## 114 -1 -0.1240654 2.107680542 TRUE
## 115 1 -0.1240654 0.390030517 FALSE
## 116 -1 -0.1240654 0.760239554 TRUE
## 117 -1 -0.1240654 0.297103552 TRUE
## 118 -1 -0.1240654 -0.031138468 TRUE
## 119 -1 -0.1240654 1.188902649 TRUE
## 120 -1 -0.1240654 1.088481574 TRUE
## 121 -1 -0.1240654 0.348063501 TRUE
## 122 -1 -0.1240654 -0.651650781 FALSE
## 123 -1 -0.1240654 2.453908427 TRUE
## 124 -1 -0.1240654 0.313590594 TRUE
## 125 1 -0.1240654 0.662816123 FALSE
## 126 -1 -0.1240654 -0.927434031 FALSE
## 127 -1 -0.1240654 -0.182519491 TRUE
## 128 -1 -0.1240654 0.782721884 TRUE
## 129 3 -0.1240654 2.013254755 TRUE
## 130 2 -0.1240654 0.190687190 FALSE
## 131 -1 -0.1240654 1.735972683 TRUE
## 132 -1 -0.1240654 -0.270949990 FALSE
## 133 -1 -0.1240654 -0.339895802 FALSE
## 134 -1 -0.1240654 -0.684624865 FALSE
## 135 2 -0.1240654 1.466184721 TRUE
## 136 2 -0.1240654 2.035737085 TRUE
## 137 1 -0.1240654 -0.954412827 FALSE
## 138 -1 -0.1240654 1.984777137 TRUE
## 139 -1 -0.1240654 -0.780549474 FALSE
## 140 -1 -0.1240654 1.274335504 TRUE
## 141 -1 -0.1240654 -0.438818055 FALSE
## 142 -1 -0.1240654 1.680516269 TRUE
## 143 -1 -0.1240654 0.166706037 TRUE
## 144 -1 -0.1240654 1.155928565 TRUE
## 145 1 -0.1240654 1.569603440 TRUE
## 146 -1 -0.1240654 -1.209212569 FALSE
## 147 -1 -0.1240654 -0.922937565 FALSE
## 148 -1 -0.1240654 -0.714601305 FALSE
## 149 -1 -0.1240654 0.151717817 TRUE
## 150 -1 -0.1240654 0.316588239 TRUE
## 151 -1 -0.1240654 -0.279942922 FALSE
## 152 -1 -0.1240654 -1.128276180 FALSE
## 153 1 -0.1240654 1.245857886 TRUE
## 154 -1 -0.1240654 -1.438532337 FALSE
## 155 -1 -0.1240654 -1.480499353 FALSE
## 156 -1 -0.1240654 -0.094088993 TRUE
## 157 -1 -0.1240654 -0.827012956 FALSE
## 158 -1 -0.1240654 0.319585883 TRUE
## 159 1 -0.1240654 1.818407894 TRUE
## 160 -1 -0.1240654 -1.074318588 FALSE
## 161 -1 -0.1240654 -0.097086637 TRUE
## 162 -1 -0.1240654 1.807916140 TRUE
## 163 -1 -0.1240654 -0.909448167 FALSE
## 164 3 -0.1240654 0.719771360 FALSE
## 165 -1 -0.1240654 -1.057831546 FALSE
## 166 -1 -0.1240654 0.297103552 TRUE
## 167 -1 -0.1240654 0.896632357 TRUE
## 168 -1 -0.1240654 -0.207999465 TRUE
## 169 -1 -0.1240654 0.138228419 TRUE
## 170 3 -0.1240654 3.618493129 TRUE
## 171 -1 -0.1240654 -0.731088347 FALSE
## 172 -1 -0.1240654 1.653537473 TRUE
## 173 -1 -0.1240654 -1.434035871 FALSE
## 174 -1 -0.1240654 0.168204859 TRUE
## 175 -1 -0.1240654 -0.410340437 FALSE
## 176 -1 -0.1240654 -1.963120041 FALSE
## 177 1 -0.1240654 0.022819124 FALSE
## 178 2 -0.1240654 2.008758289 TRUE
## 179 -1 -0.1240654 0.336072925 TRUE
## 180 -1 -0.1240654 -0.864483506 FALSE
## 181 -1 -0.1240654 -0.100084281 TRUE
## 182 3 -0.1240654 1.184406183 TRUE
## 183 -1 -0.1240654 -1.111789138 FALSE
## 184 -1 -0.1240654 -1.086309164 FALSE
## 185 -1 -0.1240654 -0.500269758 FALSE
## 186 -1 -0.1240654 1.658033939 TRUE
## 187 -1 -0.1240654 -0.656147247 FALSE
## 188 2 -0.1240654 0.360054077 FALSE
## 189 2 -0.1240654 1.694005667 TRUE
## 190 -1 -0.1240654 0.569889158 TRUE
## 191 -1 -0.1240654 0.929606441 TRUE
## 192 1 -0.1240654 1.071994532 TRUE
## 193 -1 -0.1240654 0.135230775 TRUE
## 194 -1 -0.1240654 -0.335399336 FALSE
## 195 -1 -0.1240654 -0.892961125 FALSE
## 196 3 -0.1240654 0.970074635 FALSE
## 197 -1 -0.1240654 0.554900938 TRUE
## 198 -1 -0.1240654 -2.418761932 FALSE
## 199 3 -0.1240654 2.465899003 TRUE
## 200 2 -0.1240654 0.485955126 FALSE
## 201 -1 -0.1240654 -1.098299740 FALSE
## 202 2 -0.1240654 1.314803698 TRUE
## 203 1 -0.1240654 2.641261178 TRUE
## 204 -1 -0.1240654 -0.689121331 FALSE
## 205 -1 -0.1240654 -0.134557187 TRUE
## 206 -1 -0.1240654 -2.080028158 FALSE
## 207 -1 -0.1240654 -1.278158382 FALSE
## 208 -1 -0.1240654 -0.876474082 FALSE
## 209 -1 -0.1240654 -1.582419250 FALSE
## 210 3 -0.1240654 3.610999019 TRUE
## 211 -1 -0.1240654 1.795925564 TRUE
## 212 3 -0.1240654 0.001835616 FALSE
## 213 -1 -0.1240654 -0.026642002 TRUE
## 214 -1 -0.1240654 0.785719528 TRUE
## 215 -1 -0.1240654 -1.327619508 FALSE
## 216 -1 -0.1240654 -0.894459947 FALSE
## 217 -1 -0.1240654 -0.958909293 FALSE
## 218 -1 -0.1240654 -0.771556542 FALSE
## 219 -1 -0.1240654 0.811199502 TRUE
## 220 -1 -0.1240654 -1.408555897 FALSE
## 221 -1 -0.1240654 0.418508135 TRUE
## 222 -1 -0.1240654 -0.095587815 TRUE
## 223 -1 -0.1240654 -0.128561899 TRUE
## 224 1 -0.1240654 -0.190013601 FALSE
## 225 -1 -0.1240654 -0.450808631 FALSE
## 226 -1 -0.1240654 -0.288935854 FALSE
## 227 1 -0.1240654 -0.537740308 FALSE
## 228 1 -0.1240654 -0.014651426 FALSE
## 229 -1 -0.1240654 1.274335504 TRUE
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## 1869 -1 -0.1240654 -0.994881021 FALSE
## 1870 -1 -0.1240654 0.423004601 TRUE
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## 1872 -1 -0.1240654 0.249141248 TRUE
## 1873 3 -0.1240654 0.192186012 FALSE
## 1874 -1 -0.1240654 0.458976330 TRUE
## 1875 -1 -0.1240654 -0.073105484 TRUE
## 1876 -1 -0.1240654 0.079774361 TRUE
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## 1878 3 -0.1240654 0.920613509 FALSE
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## 1880 -1 -0.1240654 0.184691902 TRUE
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## 1884 -1 -0.1240654 -0.999377487 FALSE
## 1885 1 -0.1240654 1.362766002 TRUE
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## 1888 -1 -0.1240654 0.941597017 TRUE
## 1889 2 -0.1240654 1.137942701 TRUE
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## 1892 1 -0.1240654 1.416723595 TRUE
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## 1899 3 -0.1240654 2.157141668 TRUE
## 1900 -1 -0.1240654 0.103755513 TRUE
## 1901 -1 -0.1240654 -0.584203790 FALSE
## 1902 -1 -0.1240654 0.310592950 TRUE
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## 1904 1 -0.1240654 0.229656562 FALSE
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## 1908 -1 -0.1240654 -0.783547118 FALSE
## 1909 -1 -0.1240654 -1.243685475 FALSE
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## 1911 3 -0.1240654 0.935601729 FALSE
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## 1914 3 -0.1240654 1.091479218 FALSE
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## 1917 -1 -0.1240654 0.838178298 TRUE
## 1918 -1 -0.1240654 0.556399760 TRUE
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## 1922 3 -0.1240654 0.952088771 FALSE
## 1923 -1 -0.1240654 -0.999377487 FALSE
## 1924 1 -0.1240654 0.397524627 FALSE
## 1925 1 -0.1240654 -0.029639646 FALSE
## 1926 -1 -0.1240654 -0.970899869 FALSE
## 1927 -1 -0.1240654 -0.669636645 FALSE
## 1928 -1 -0.1240654 -0.070107840 TRUE
## 1929 -1 -0.1240654 -0.004159672 TRUE
## 1930 -1 -0.1240654 -0.859987040 FALSE
## 1931 1 -0.1240654 0.421505779 FALSE
## 1932 -1 -0.1240654 0.496446880 TRUE
## 1933 -1 -0.1240654 0.132233131 TRUE
## 1934 -1 -0.1240654 0.391529339 TRUE
## 1935 3 -0.1240654 2.627771780 TRUE
## 1936 -1 -0.1240654 -0.813523558 FALSE
## 1937 -1 -0.1240654 -1.592911004 FALSE
## 1938 -1 -0.1240654 -1.571927496 FALSE
## 1939 -1 -0.1240654 -0.326406404 FALSE
## 1940 -1 -0.1240654 -0.276945278 FALSE
## 1941 -1 -0.1240654 0.280616510 TRUE
## 1942 2 -0.1240654 0.842674764 FALSE
## 1943 -1 -0.1240654 0.271623578 TRUE
## 1944 -1 -0.1240654 0.926608797 TRUE
## 1945 -1 -0.1240654 0.926608797 TRUE
## 1946 -1 -0.1240654 -0.668137823 FALSE
## 1947 -1 -0.1240654 -0.862984684 FALSE
## 1948 -1 -0.1240654 -0.948417539 FALSE
## 1949 -1 -0.1240654 -0.707107195 FALSE
## 1950 -1 -0.1240654 -0.653149603 FALSE
## 1951 -1 -0.1240654 -0.804530626 FALSE
## 1952 -1 -0.1240654 -0.952914005 FALSE
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## 1954 -1 -0.1240654 -1.374082990 FALSE
## 1955 -1 -0.1240654 0.361552899 TRUE
## 1956 -1 -0.1240654 -0.414836903 FALSE
## 1957 -1 -0.1240654 -0.270949990 FALSE
## 1958 -1 -0.1240654 0.910121755 TRUE
## 1959 -1 -0.1240654 -0.945419895 FALSE
## 1960 -1 -0.1240654 -0.633664917 FALSE
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## 1963 -1 -0.1240654 0.418508135 TRUE
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## 1965 -1 -0.1240654 -0.170528915 TRUE
## 1966 -1 -0.1240654 0.091764937 TRUE
## 1967 -1 -0.1240654 -1.775767289 FALSE
## 1968 -1 -0.1240654 -1.000876309 FALSE
## 1969 1 -0.1240654 1.722483285 TRUE
## 1970 3 -0.1240654 2.573814188 TRUE
## 1971 -1 -0.1240654 -0.780549474 FALSE
## 1972 -1 -0.1240654 0.907124111 TRUE
## 1973 -1 -0.1240654 -0.290434676 FALSE
## 1974 -1 -0.1240654 -0.892961125 FALSE
## 1975 -1 -0.1240654 -0.743078923 FALSE
## 1976 -1 -0.1240654 -0.868979972 FALSE
## 1977 -1 -0.1240654 -0.711603661 FALSE
## 1978 -1 -0.1240654 -0.675631933 FALSE
## 1979 -1 -0.1240654 -1.156753799 FALSE
## 1980 -1 -0.1240654 -1.194224349 FALSE
## 1981 -1 -0.1240654 -0.609683764 FALSE
## 1982 -1 -0.1240654 0.313590594 TRUE
## 1983 -1 -0.1240654 -0.849495286 FALSE
## 1984 -1 -0.1240654 -0.317413472 FALSE
## 1985 -1 -0.1240654 0.285112976 TRUE
## 1986 -1 -0.1240654 -1.045840970 FALSE
## 1987 -1 -0.1240654 -0.142051297 TRUE
## 1988 -1 -0.1240654 -0.467295673 FALSE
## 1989 -1 -0.1240654 -0.476288605 FALSE
## 1990 -1 -0.1240654 1.503655271 TRUE
## 1991 -1 -0.1240654 -0.777551830 FALSE
## 1992 -1 -0.1240654 1.722483285 TRUE
## 1993 -1 -0.1240654 -0.639660205 FALSE
## 1994 -1 -0.1240654 0.252138892 TRUE
## 1995 -1 -0.1240654 -0.521253266 FALSE
## 1996 -1 -0.1240654 0.420006957 TRUE
## 1997 -1 -0.1240654 0.524924498 TRUE
## 1998 -1 -0.1240654 -0.767060076 FALSE
## 1999 -1 -0.1240654 -1.264668983 FALSE
## 2000 -1 -0.1240654 -0.294931142 FALSE
## 2001 -1 -0.1240654 0.677804343 TRUE
## 2002 -1 -0.1240654 0.165207215 TRUE
## 2003 -1 -0.1240654 -1.011368064 FALSE
## 2004 -1 -0.1240654 -0.158538339 TRUE
## 2005 -1 -0.1240654 -0.696615441 FALSE
## 2006 2 -0.1240654 2.674235262 TRUE
## 2007 1 -0.1240654 1.383749511 TRUE
## 2008 -1 -0.1240654 0.487453948 TRUE
## 2009 -1 -0.1240654 0.135230775 TRUE
## 2010 -1 -0.1240654 -0.273947634 FALSE
## 2011 -1 -0.1240654 -1.257174873 FALSE
## 2012 -1 -0.1240654 0.967076991 TRUE
## 2013 -1 -0.1240654 -0.979892801 FALSE
## 2014 1 -0.1240654 0.433496355 FALSE
## 2015 1 -0.1240654 1.613069278 TRUE
## 2016 3 -0.1240654 0.105254335 FALSE
## 2017 -1 -0.1240654 -1.194224349 FALSE
## 2018 -1 -0.1240654 0.451482220 TRUE
## 2019 -1 -0.1240654 -0.656147247 FALSE
## 2020 3 -0.1240654 2.034238263 TRUE
## 2021 -1 -0.1240654 -0.985888089 FALSE
## 2022 -1 -0.1240654 0.554900938 TRUE
## 2023 -1 -0.1240654 1.284827258 TRUE
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## 2027 -1 -0.1240654 -0.537740308 FALSE
## 2028 -1 -0.1240654 -0.410340437 FALSE
## 2029 3 -0.1240654 0.434995177 FALSE
## 2030 -1 -0.1240654 -1.066824478 FALSE
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## 2032 -1 -0.1240654 -0.518255622 FALSE
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## 2034 -1 -0.1240654 0.136729597 TRUE
## 2035 -1 -0.1240654 1.613069278 TRUE
## 2036 -1 -0.1240654 0.267127112 TRUE
## 2037 -1 -0.1240654 0.878646493 TRUE
## 2038 -1 -0.1240654 0.183193080 TRUE
## 2039 -1 -0.1240654 -1.068323300 FALSE
## 2040 1 -0.1240654 0.749747800 FALSE
## 2041 -1 -0.1240654 -0.732587169 FALSE
## 2042 1 -0.1240654 0.195183656 FALSE
## 2043 1 -0.1240654 -0.886965837 FALSE
## 2044 -1 -0.1240654 0.902627645 TRUE
## 2045 -1 -0.1240654 -1.180734951 FALSE
## 2046 -1 -0.1240654 0.157713105 TRUE
## 2047 -1 -0.1240654 0.875648849 TRUE
## 2048 3 -0.1240654 0.159211927 FALSE
## 2049 -1 -0.1240654 -0.889963481 FALSE
## 2050 2 -0.1240654 0.361552899 FALSE
## 2051 2 -0.1240654 0.369047009 FALSE
## 2052 -1 -0.1240654 0.595369133 TRUE
## 2053 -1 -0.1240654 -0.865982328 FALSE
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## 2055 -1 -0.1240654 -0.059616086 TRUE
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## 2057 -1 -0.1240654 -0.449309809 FALSE
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## 2059 3 -0.1240654 -0.837504710 FALSE
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## 2061 -1 -0.1240654 -1.537454589 FALSE
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## 2064 -1 -0.1240654 1.587589304 TRUE
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## 2067 1 -0.1240654 1.628057498 TRUE
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## 2070 -1 -0.1240654 -0.828511778 FALSE
## 2071 -1 -0.1240654 -0.765561254 FALSE
## 2072 -1 -0.1240654 -1.069822122 FALSE
## 2073 3 -0.1240654 1.317801342 TRUE
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## 2088 -1 -0.1240654 0.914618221 TRUE
## 2089 -1 -0.1240654 -0.819518846 FALSE
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## 2091 1 -0.1240654 2.167633422 TRUE
## 2092 -1 -0.1240654 1.788431454 TRUE
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## 2097 -1 -0.1240654 1.406231841 TRUE
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## 2100 1 -0.1240654 0.577383268 FALSE
## 2101 -1 -0.1240654 -0.252964126 TRUE
## 2102 -1 -0.1240654 1.173914429 TRUE
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## 2104 -1 -0.1240654 -0.828511778 FALSE
## 2105 2 -0.1240654 1.283328436 TRUE
## 2106 1 -0.1240654 2.043231196 TRUE
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## 2108 1 -0.1240654 1.801920852 TRUE
## 2109 -1 -0.1240654 -1.476002887 FALSE
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## 2115 -1 -0.1240654 -0.411839259 FALSE
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## 2120 -1 -0.1240654 -0.251465304 TRUE
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## 2122 -1 -0.1240654 -0.353385201 FALSE
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## 2127 -1 -0.1240654 -0.819518846 FALSE
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## 2140 -1 -0.1240654 -0.561721460 FALSE
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## 2142 -1 -0.1240654 1.406231841 TRUE
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## 2148 -1 -0.1240654 0.316588239 TRUE
## 2149 -1 -0.1240654 0.198181300 TRUE
## 2150 -1 -0.1240654 -0.537740308 FALSE
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## 2155 -1 -0.1240654 -0.738582457 FALSE
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## 2157 3 -0.1240654 1.151432099 TRUE
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## 2172 -1 -0.1240654 0.868154739 TRUE
## 2173 -1 -0.1240654 -0.743078923 FALSE
## 2174 -1 -0.1240654 -1.114786782 FALSE
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## 2176 -1 -0.1240654 0.925109975 TRUE
## 2177 -1 -0.1240654 -0.785045940 FALSE
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## 2181 -1 -0.1240654 1.391243621 TRUE
## 2182 -1 -0.1240654 -0.510761512 FALSE
## 2183 -1 -0.1240654 -0.852492930 FALSE
## 2184 3 -0.1240654 0.111249623 FALSE
## 2185 1 -0.1240654 1.355271892 TRUE
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## 2190 -1 -0.1240654 -0.573712036 FALSE
## 2191 -1 -0.1240654 -0.007157316 TRUE
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## 2200 -1 -0.1240654 -1.017363352 FALSE
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## 2221 -1 -0.1240654 1.647542185 TRUE
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## 2227 -1 -0.1240654 -0.928932853 FALSE
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## 2240 -1 -0.1240654 -0.895958769 FALSE
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## 2681 -1 -0.1240654 -0.561721460 FALSE
## 2682 -1 -0.1240654 -0.098585459 TRUE
## 2683 -1 -0.1240654 -0.737083635 FALSE
## 2684 -1 -0.1240654 -1.009869241 FALSE
## 2685 -1 -0.1240654 -0.888464659 FALSE
## 2686 -1 -0.1240654 0.548905650 TRUE
## 2687 -1 -0.1240654 -0.306921718 FALSE
## 2688 -1 -0.1240654 -0.836005888 FALSE
## 2689 -1 -0.1240654 1.942810121 TRUE
## 2690 -1 -0.1240654 0.811199502 TRUE
## 2691 -1 -0.1240654 -0.756568322 FALSE
## 2692 -1 -0.1240654 0.241647138 TRUE
## 2693 1 -0.1240654 1.924824257 TRUE
## 2694 -1 -0.1240654 0.388531695 TRUE
## 2695 -1 -0.1240654 -0.010154960 TRUE
## 2696 -1 -0.1240654 -0.677130755 FALSE
## 2697 -1 -0.1240654 0.078275539 TRUE
## 2698 -1 -0.1240654 -0.248467660 TRUE
## 2699 -1 -0.1240654 0.478461016 TRUE
## 2700 -1 -0.1240654 0.396025805 TRUE
## 2701 -1 -0.1240654 0.922112331 TRUE
## 2702 -1 -0.1240654 -0.743078923 FALSE
## 2703 -1 -0.1240654 0.055793209 TRUE
## 2704 -1 -0.1240654 -0.894459947 FALSE
## 2705 -1 -0.1240654 -1.323123042 FALSE
## 2706 -1 -0.1240654 -1.042843326 FALSE
## 2707 -1 -0.1240654 0.034809700 TRUE
## 2708 -1 -0.1240654 1.743466793 TRUE
## 2709 -1 -0.1240654 1.238363775 TRUE
## 2710 -1 -0.1240654 -1.194224349 FALSE
## 2711 -1 -0.1240654 0.048299098 TRUE
## 2712 -1 -0.1240654 -0.299427608 FALSE
## 2713 2 -0.1240654 1.640048074 TRUE
## 2714 -1 -0.1240654 0.797710104 TRUE
## 2715 -1 -0.1240654 0.593870311 TRUE
## 2716 -1 -0.1240654 -0.755069500 FALSE
## 2717 -1 -0.1240654 0.638834971 TRUE
## 2718 2 -0.1240654 2.158640490 TRUE
## 2719 -1 -0.1240654 0.856164163 TRUE
## 2720 -1 -0.1240654 1.259347284 TRUE
## 2721 -1 -0.1240654 -0.927434031 FALSE
## 2722 -1 -0.1240654 1.169417963 TRUE
## 2723 -1 -0.1240654 0.437992821 TRUE
## 2724 -1 -0.1240654 -0.148046585 TRUE
## 2725 3 -0.1240654 1.541125822 TRUE
## 2726 -1 -0.1240654 -0.858488218 FALSE
## 2727 3 -0.1240654 2.705710525 TRUE
## 2728 -1 -0.1240654 -0.605187298 FALSE
## 2729 3 -0.1240654 2.555828323 TRUE
## 2730 -1 -0.1240654 -0.798535338 FALSE
## 2731 -1 -0.1240654 -0.464298029 FALSE
## 2732 -1 -0.1240654 -1.327619508 FALSE
## 2733 -1 -0.1240654 -0.803031804 FALSE
## 2734 -1 -0.1240654 -0.273947634 FALSE
## 2735 -1 -0.1240654 -0.770057720 FALSE
## 2736 -1 -0.1240654 0.979067568 TRUE
## 2737 -1 -0.1240654 -0.828511778 FALSE
## 2738 -1 -0.1240654 -0.181020669 TRUE
## 2739 -1 -0.1240654 -0.041630222 TRUE
## 2740 -1 -0.1240654 1.661031583 TRUE
## 2741 -1 -0.1240654 -0.422331013 FALSE
## 2742 -1 -0.1240654 -1.535955767 FALSE
## 2743 1 -0.1240654 1.214382623 TRUE
## 2744 -1 -0.1240654 0.304597662 TRUE
## 2745 1 -0.1240654 1.608572812 TRUE
## 2746 -1 -0.1240654 0.563893870 TRUE
## 2747 2 -0.1240654 1.768946767 TRUE
## 2748 -1 -0.1240654 -0.623173163 FALSE
## 2749 -1 -0.1240654 -0.957410471 FALSE
## 2750 2 -0.1240654 1.051011024 TRUE
## 2751 -1 -0.1240654 -0.693617797 FALSE
## 2752 -1 -0.1240654 -0.861485862 FALSE
## 2753 -1 -0.1240654 1.063001600 TRUE
## 2754 -1 -0.1240654 -0.785045940 FALSE
## 2755 -1 -0.1240654 -0.630667273 FALSE
## 2756 2 -0.1240654 1.616066922 TRUE
## 2757 -1 -0.1240654 -0.662142535 FALSE
## 2758 -1 -0.1240654 -0.702610729 FALSE
## 2759 3 -0.1240654 0.321084705 FALSE
## 2760 -1 -0.1240654 1.954800697 TRUE
## 2761 -1 -0.1240654 -0.726591881 FALSE
## 2762 2 -0.1240654 2.163136956 TRUE
## 2763 -1 -0.1240654 -1.517969903 FALSE
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## 2765 1 -0.1240654 0.982065212 TRUE
## 2766 -1 -0.1240654 -0.680128399 FALSE
## 2767 -1 -0.1240654 -0.107578391 TRUE
## 2768 -1 -0.1240654 -1.092304452 FALSE
## 2769 -1 -0.1240654 -0.719097771 FALSE
## 2770 -1 -0.1240654 0.731761936 TRUE
## 2771 -1 -0.1240654 -0.246968838 TRUE
## 2772 -1 -0.1240654 0.219164808 TRUE
## 2773 -1 -0.1240654 -0.994881021 FALSE
## 2774 1 -0.1240654 1.101970972 TRUE
## 2775 -1 -0.1240654 1.526137602 TRUE
## 2776 -1 -0.1240654 -0.768558898 FALSE
## 2777 -1 -0.1240654 -0.833008244 FALSE
## 2778 -1 -0.1240654 -0.925935209 FALSE
## 2779 -1 -0.1240654 -0.840502354 FALSE
## 2780 -1 -0.1240654 0.823190078 TRUE
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## 2784 -1 -0.1240654 -0.708606017 FALSE
## 2785 -1 -0.1240654 0.348063501 TRUE
## 2786 -1 -0.1240654 -0.818020024 FALSE
## 2787 1 -0.1240654 1.711991531 TRUE
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## 2789 -1 -0.1240654 0.641832615 TRUE
## 2790 -1 -0.1240654 0.989559322 TRUE
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## 2793 -1 -0.1240654 -0.624671985 FALSE
## 2794 -1 -0.1240654 0.199680122 TRUE
## 2795 -1 -0.1240654 -0.807528270 FALSE
## 2796 -1 -0.1240654 -0.339895802 FALSE
## 2797 -1 -0.1240654 -0.644156671 FALSE
## 2798 -1 -0.1240654 0.229656562 TRUE
## 2799 -1 -0.1240654 -0.636662561 FALSE
## 2800 3 -0.1240654 3.654464858 TRUE
## 2801 -1 -0.1240654 -0.465796851 FALSE
## 2802 2 -0.1240654 1.433210637 TRUE
## 2803 -1 -0.1240654 -0.288935854 FALSE
## 2804 -1 -0.1240654 0.910121755 TRUE
## 2805 -1 -0.1240654 -0.173526559 TRUE
## 2806 -1 -0.1240654 -0.196008889 TRUE
## 2807 -1 -0.1240654 -0.898956413 FALSE
## 2808 -1 -0.1240654 0.303098840 TRUE
## 2809 -1 -0.1240654 0.076776717 TRUE
## 2810 -1 -0.1240654 -0.494274470 FALSE
## 2811 -1 -0.1240654 -0.879471727 FALSE
## 2812 -1 -0.1240654 -1.233193721 FALSE
## 2813 3 -0.1240654 0.854665341 FALSE
## 2814 -1 -0.1240654 -0.720596593 FALSE
## 2815 -1 -0.1240654 0.652324369 TRUE
## 2816 -1 -0.1240654 -0.561721460 FALSE
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## 2819 -1 -0.1240654 -0.728090703 FALSE
## 2820 -1 -0.1240654 -0.729589525 FALSE
## 2821 -1 -0.1240654 -0.639660205 FALSE
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## 2823 -1 -0.1240654 0.316588239 TRUE
## 2824 -1 -0.1240654 -0.357881667 FALSE
## 2825 -1 -0.1240654 -0.324907582 FALSE
## 2826 1 -0.1240654 -0.094088993 FALSE
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## 2828 -1 -0.1240654 1.170916785 TRUE
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## 2830 -1 -0.1240654 -0.741580101 FALSE
## 2831 -1 -0.1240654 -0.765561254 FALSE
## 2832 -1 -0.1240654 -1.222701967 FALSE
## 2833 -1 -0.1240654 -0.735584813 FALSE
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## 2838 -1 -0.1240654 -1.408555897 FALSE
## 2839 -1 -0.1240654 -1.471506421 FALSE
## 2840 -1 -0.1240654 0.994055788 TRUE
## 2841 -1 -0.1240654 0.556399760 TRUE
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## 2843 -1 -0.1240654 0.042303810 TRUE
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## 2845 -1 -0.1240654 0.800707748 TRUE
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## 2848 1 -0.1240654 0.363051721 FALSE
## 2849 -1 -0.1240654 -0.970899869 FALSE
## 2850 1 -0.1240654 0.051296743 FALSE
## 2851 -1 -0.1240654 -0.624671985 FALSE
## 2852 -1 -0.1240654 -0.554227350 FALSE
## 2853 -1 -0.1240654 0.184691902 TRUE
## 2854 1 -0.1240654 0.324082349 FALSE
## 2855 -1 -0.1240654 -0.789542406 FALSE
## 2856 -1 -0.1240654 -0.936426963 FALSE
## 2857 1 -0.1240654 0.916117043 TRUE
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## 2861 -1 -0.1240654 -1.186730239 FALSE
## 2862 -1 -0.1240654 -0.092590171 TRUE
## 2863 -1 -0.1240654 1.505154093 TRUE
## 2864 3 -0.1240654 0.412512847 FALSE
## 2865 -1 -0.1240654 -0.795537694 FALSE
## 2866 -1 -0.1240654 -0.846497642 FALSE
## 2867 -1 -0.1240654 0.598366777 TRUE
## 2868 3 -0.1240654 1.683513913 TRUE
## 2869 1 -0.1240654 0.111249623 FALSE
## 2870 -1 -0.1240654 1.290822546 TRUE
## 2871 -1 -0.1240654 -0.624671985 FALSE
## 2872 -1 -0.1240654 -0.928932853 FALSE
## 2873 -1 -0.1240654 -0.846497642 FALSE
## 2874 -1 -0.1240654 1.215881445 TRUE
## 2875 -1 -0.1240654 -0.453806275 FALSE
## 2876 -1 -0.1240654 -0.248467660 TRUE
## 2877 -1 -0.1240654 0.364550543 TRUE
## 2878 -1 -0.1240654 -0.952914005 FALSE
## 2879 -1 -0.1240654 0.306096484 TRUE
## 2880 -1 -0.1240654 -0.251465304 TRUE
## 2881 -1 -0.1240654 -0.606686120 FALSE
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## 2883 -1 -0.1240654 -0.898956413 FALSE
## 2884 -1 -0.1240654 -0.825514134 FALSE
## 2885 -1 -0.1240654 0.535416252 TRUE
## 2886 -1 -0.1240654 -0.870478794 FALSE
## 2887 -1 -0.1240654 0.087268471 TRUE
## 2888 -1 -0.1240654 0.411014025 TRUE
## 2889 -1 -0.1240654 1.514147026 TRUE
## 2890 -1 -0.1240654 -1.603402758 FALSE
## 2891 1 -0.1240654 0.052795565 FALSE
## 2892 -1 -0.1240654 -1.985602371 FALSE
## 2893 3 -0.1240654 1.281829614 TRUE
## 2894 -1 -0.1240654 -0.728090703 FALSE
## 2895 -1 -0.1240654 -0.815022380 FALSE
## 2896 -1 -0.1240654 0.431997533 TRUE
## 2897 -1 -0.1240654 -0.847996464 FALSE
## 2898 -1 -0.1240654 -0.957410471 FALSE
## 2899 -1 -0.1240654 0.255136536 TRUE
## 2900 2 -0.1240654 0.646329081 FALSE
## 2901 3 -0.1240654 1.301314300 TRUE
## 2902 -1 -0.1240654 0.781223062 TRUE
## 2903 -1 -0.1240654 -0.994881021 FALSE
## 2904 -1 -0.1240654 -0.889963481 FALSE
## 2905 -1 -0.1240654 0.760239554 TRUE
## 2906 2 -0.1240654 1.888852528 TRUE
## 2907 -1 -0.1240654 -1.320125398 FALSE
## 2908 -1 -0.1240654 0.276120044 TRUE
## 2909 -1 -0.1240654 -0.070107840 TRUE
## 2910 -1 -0.1240654 -0.862984684 FALSE
## 2911 -1 -0.1240654 -0.794038872 FALSE
## 2912 1 -0.1240654 2.876576234 TRUE
## 2913 -1 -0.1240654 0.112748445 TRUE
## 2914 -1 -0.1240654 -0.921438743 FALSE
## 2915 -1 -0.1240654 0.590872667 TRUE
## 2916 -1 -0.1240654 -0.943921073 FALSE
## 2917 2 -0.1240654 1.021034584 FALSE
## 2918 2 -0.1240654 1.169417963 TRUE
## 2919 -1 -0.1240654 -0.840502354 FALSE
## 2920 -1 -0.1240654 0.127736665 TRUE
## 2921 -1 -0.1240654 -0.876474082 FALSE
## 2922 -1 -0.1240654 -0.383361641 FALSE
## 2923 -1 -0.1240654 0.704783139 TRUE
## 2924 -1 -0.1240654 -0.692118975 FALSE
## 2925 -1 -0.1240654 -0.014651426 TRUE
## 2926 -1 -0.1240654 -1.326120686 FALSE
## 2927 -1 -0.1240654 -1.446026447 FALSE
## 2928 3 -0.1240654 2.843602150 TRUE
## 2929 -1 -0.1240654 0.159211927 TRUE
## 2930 -1 -0.1240654 -1.673847393 FALSE
## 2931 -1 -0.1240654 -0.221488864 TRUE
## 2932 2 -0.1240654 1.371758934 TRUE
## 2933 1 -0.1240654 1.993770069 TRUE
## 2934 -1 -0.1240654 -0.600690832 FALSE
## 2935 -1 -0.1240654 -0.251465304 TRUE
## 2936 -1 -0.1240654 -0.695116619 FALSE
## 2937 -1 -0.1240654 -0.936426963 FALSE
## 2938 -1 -0.1240654 -0.167531271 TRUE
## 2939 -1 -0.1240654 -1.159751443 FALSE
## 2940 1 -0.1240654 1.503655271 TRUE
## 2941 -1 -0.1240654 -1.045840970 FALSE
## 2942 -1 -0.1240654 1.077989820 TRUE
## 2943 2 -0.1240654 1.535130534 TRUE
## 2944 -1 -0.1240654 -1.374082990 FALSE
## 2945 -1 -0.1240654 0.280616510 TRUE
## 2946 -1 -0.1240654 -0.585702612 FALSE
## 2947 -1 -0.1240654 0.448484576 TRUE
## 2948 -1 -0.1240654 -0.270949990 FALSE
## 2949 -1 -0.1240654 0.214668342 TRUE
## 2950 1 -0.1240654 0.265628290 FALSE
## 2951 1 -0.1240654 0.093263759 FALSE
## 2952 -1 -0.1240654 -1.215207857 FALSE
## 2953 -1 -0.1240654 -0.699613085 FALSE
## 2954 -1 -0.1240654 1.185905005 TRUE
## 2955 1 -0.1240654 0.613354997 FALSE
## 2956 -1 -0.1240654 -0.743078923 FALSE
## 2957 -1 -0.1240654 -0.916942277 FALSE
## 2958 -1 -0.1240654 0.352559967 TRUE
## 2959 -1 -0.1240654 -0.137554831 TRUE
## 2960 -1 -0.1240654 -1.739795561 FALSE
## 2961 1 -0.1240654 1.131947413 TRUE
## 2962 -1 -0.1240654 -0.506265046 FALSE
## 2963 -1 -0.1240654 -0.624671985 FALSE
## 2964 2 -0.1240654 1.424217705 TRUE
## 2965 -1 -0.1240654 0.072280251 TRUE
## 2966 -1 -0.1240654 -0.946918717 FALSE
## 2967 -1 -0.1240654 1.359768358 TRUE
## 2968 -1 -0.1240654 -0.762563610 FALSE
## 2969 -1 -0.1240654 -0.825514134 FALSE
## 2970 -1 -0.1240654 -1.194224349 FALSE
## 2971 2 -0.1240654 1.458690611 TRUE
## 2972 -1 -0.1240654 1.682015091 TRUE
## 2973 2 -0.1240654 1.992271247 TRUE
## 2974 -1 -0.1240654 -0.930431675 FALSE
## 2975 1 -0.1240654 -0.996379843 FALSE
## 2976 -1 -0.1240654 -1.329118330 FALSE
## 2977 3 -0.1240654 2.322012090 TRUE
## 2978 3 -0.1240654 0.265628290 FALSE
## 2979 -1 -0.1240654 0.354058789 TRUE
## 2980 1 -0.1240654 0.992556966 TRUE
## 2981 1 -0.1240654 0.426002245 FALSE
## 2982 3 -0.1240654 1.322297808 TRUE
## 2983 1 -0.1240654 0.632839683 FALSE
## 2984 -1 -0.1240654 0.553402116 TRUE
## 2985 -1 -0.1240654 0.653823191 TRUE
## 2986 3 -0.1240654 1.788431454 TRUE
## 2987 2 -0.1240654 0.859161807 FALSE
## 2988 1 -0.1240654 1.647542185 TRUE
## 2989 -1 -0.1240654 0.046800276 TRUE
## 2990 2 -0.1240654 2.131661694 TRUE
## 2991 -1 -0.1240654 -1.422045295 FALSE
## 2992 -1 -0.1240654 -0.016150248 TRUE
## 2993 -1 -0.1240654 -0.791041228 FALSE
## 2994 -1 -0.1240654 -0.178023025 TRUE
## 2995 -1 -0.1240654 -1.795251975 FALSE
## 2996 -1 -0.1240654 -1.763776713 FALSE
## 2997 -1 -0.1240654 -0.584203790 FALSE
## 2998 -1 -0.1240654 -0.753570678 FALSE
## 2999 -1 -0.1240654 1.154429743 TRUE
## 3000 -1 -0.1240654 -0.749074212 FALSE
## 3001 -1 -0.1240654 -1.306636000 FALSE
## 3002 -1 -0.1240654 1.410728307 TRUE
## 3003 -1 -0.1240654 -0.630667273 FALSE
## 3004 -1 -0.1240654 -1.165746731 FALSE
## 3005 2 -0.1240654 0.322583527 FALSE
## 3006 -1 -0.1240654 -0.773055364 FALSE
## 3007 -1 -0.1240654 -0.213994753 TRUE
## 3008 -1 -0.1240654 -1.017363352 FALSE
## 3009 -1 -0.1240654 -1.738296739 FALSE
## 3010 -1 -0.1240654 -0.372869887 FALSE
## 3011 -1 -0.1240654 -1.021859818 FALSE
## 3012 -1 -0.1240654 -0.267952346 TRUE
## 3013 1 -0.1240654 0.162209571 FALSE
## 3014 -1 -0.1240654 -0.701111907 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "mood_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "mood_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $psychosis_4factorv2
## $fit
## Hydra_k3 psychosis_4factorv2 visregFit visregLwr visregUpr
## 1 -1 -0.1829434 -0.06954718 -0.10786690 -0.03122745
## 2 1 -0.1829434 0.03794323 -0.07424194 0.15012839
## 3 2 -0.1829434 0.02062262 -0.10581072 0.14705596
## 4 3 -0.1829434 -0.08589817 -0.20494405 0.03314771
##
## $res
## Hydra_k3 psychosis_4factorv2 visregRes visregPos
## 1 -1 -0.1829434 -1.076627072 FALSE
## 2 -1 -0.1829434 -0.560276490 FALSE
## 3 -1 -0.1829434 1.765287093 TRUE
## 4 -1 -0.1829434 -0.081659220 FALSE
## 5 -1 -0.1829434 -0.337848547 FALSE
## 6 -1 -0.1829434 -0.228620539 FALSE
## 7 -1 -0.1829434 -0.429202881 FALSE
## 8 -1 -0.1829434 -0.472894084 FALSE
## 9 1 -0.1829434 0.784220987 TRUE
## 10 -1 -0.1829434 -1.229546283 FALSE
## 11 3 -0.1829434 -0.002220669 TRUE
## 12 -1 -0.1829434 -0.234578430 FALSE
## 13 1 -0.1829434 -0.953497318 FALSE
## 14 -1 -0.1829434 0.341351065 TRUE
## 15 -1 -0.1829434 0.071259991 TRUE
## 16 -1 -0.1829434 -2.375447382 FALSE
## 17 -1 -0.1829434 1.034452423 TRUE
## 18 -1 -0.1829434 0.526045696 TRUE
## 19 -1 -0.1829434 -0.792634252 FALSE
## 20 -1 -0.1829434 -0.304087163 FALSE
## 21 -1 -0.1829434 -0.312031018 FALSE
## 22 -1 -0.1829434 -0.544388780 FALSE
## 23 -1 -0.1829434 -0.304087163 FALSE
## 24 -1 -0.1829434 -0.955483282 FALSE
## 25 1 -0.1829434 2.053251841 TRUE
## 26 -1 -0.1829434 1.552788969 TRUE
## 27 -1 -0.1829434 0.371140521 TRUE
## 28 -1 -0.1829434 1.469378490 TRUE
## 29 -1 -0.1829434 -0.371609931 FALSE
## 30 -1 -0.1829434 -0.224648612 FALSE
## 31 -1 -0.1829434 1.153610250 TRUE
## 32 -1 -0.1829434 -0.037968017 TRUE
## 33 -1 -0.1829434 -0.379553786 FALSE
## 34 -1 -0.1829434 -0.256424032 FALSE
## 35 -1 -0.1829434 -0.347778366 FALSE
## 36 -1 -0.1829434 1.616339810 TRUE
## 37 -1 -0.1829434 1.457462708 TRUE
## 38 2 -0.1829434 -0.270325778 FALSE
## 39 -1 -0.1829434 -0.306073126 FALSE
## 40 -1 -0.1829434 0.919266524 TRUE
## 41 -1 -0.1829434 -0.341820474 FALSE
## 42 2 -0.1829434 1.743441491 TRUE
## 43 -1 -0.1829434 -0.157125843 FALSE
## 44 -1 -0.1829434 -0.476866011 FALSE
## 45 -1 -0.1829434 -0.325932764 FALSE
## 46 -1 -0.1829434 1.445546925 TRUE
## 47 1 -0.1829434 -0.081659220 FALSE
## 48 -1 -0.1829434 -0.143224097 FALSE
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##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "psychosis_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "psychosis_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $externalizing_4factorv2
## $fit
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## 3 2 -0.4826753 0.1220844 0.005875252 0.2382935
## 4 3 -0.4826753 0.0619370 -0.047482072 0.1713561
##
## $res
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## 1904 1 -0.4826753 0.697301141 TRUE
## 1905 -1 -0.4826753 -0.824664689 FALSE
## 1906 -1 -0.4826753 -0.212405618 TRUE
## 1907 -1 -0.4826753 -1.068511397 FALSE
## 1908 -1 -0.4826753 -0.728786882 FALSE
## 1909 -1 -0.4826753 -0.877510725 FALSE
## 1910 -1 -0.4826753 -0.669146355 FALSE
## 1911 3 -0.4826753 -0.675940846 FALSE
## 1912 -1 -0.4826753 -1.364449197 FALSE
## 1913 1 -0.4826753 -0.481920400 FALSE
## 1914 3 -0.4826753 -0.880530499 FALSE
## 1915 -1 -0.4826753 0.023891656 TRUE
## 1916 -1 -0.4826753 -0.834478953 FALSE
## 1917 -1 -0.4826753 -0.476635797 FALSE
## 1918 -1 -0.4826753 -0.949985288 FALSE
## 1919 2 -0.4826753 0.014832336 FALSE
## 1920 -1 -0.4826753 -0.943190798 FALSE
## 1921 3 -0.4826753 0.329643721 TRUE
## 1922 3 -0.4826753 0.907930340 TRUE
## 1923 -1 -0.4826753 -0.862411858 FALSE
## 1924 1 -0.4826753 1.576055221 TRUE
## 1925 1 -0.4826753 -1.309338332 FALSE
## 1926 -1 -0.4826753 0.675407783 TRUE
## 1927 -1 -0.4826753 -0.620829980 FALSE
## 1928 -1 -0.4826753 0.045030071 TRUE
## 1929 -1 -0.4826753 0.185449537 TRUE
## 1930 -1 -0.4826753 1.185749500 TRUE
## 1931 1 -0.4826753 1.243880139 TRUE
## 1932 -1 -0.4826753 -0.364904178 TRUE
## 1933 -1 -0.4826753 -0.796731785 FALSE
## 1934 -1 -0.4826753 -0.631399187 FALSE
## 1935 3 -0.4826753 -0.868451405 FALSE
## 1936 -1 -0.4826753 -0.366414065 TRUE
## 1937 -1 -0.4826753 -2.202436336 FALSE
## 1938 -1 -0.4826753 -0.297714218 TRUE
## 1939 -1 -0.4826753 1.376750172 TRUE
## 1940 -1 -0.4826753 -0.267516484 TRUE
## 1941 -1 -0.4826753 -0.716707788 FALSE
## 1942 2 -0.4826753 1.581339825 TRUE
## 1943 -1 -0.4826753 -0.589122358 FALSE
## 1944 -1 -0.4826753 -1.106258566 FALSE
## 1945 -1 -0.4826753 1.156306709 TRUE
## 1946 -1 -0.4826753 -0.654047488 FALSE
## 1947 -1 -0.4826753 -1.116827773 FALSE
## 1948 -1 -0.4826753 0.352292022 TRUE
## 1949 -1 -0.4826753 -0.568738887 FALSE
## 1950 -1 -0.4826753 -0.515137908 FALSE
## 1951 -1 -0.4826753 -0.665371639 FALSE
## 1952 -1 -0.4826753 0.042765241 TRUE
## 1953 -1 -0.4826753 -0.679715563 FALSE
## 1954 -1 -0.4826753 -1.305563615 FALSE
## 1955 -1 -0.4826753 -0.241093466 TRUE
## 1956 -1 -0.4826753 -1.141740904 FALSE
## 1957 -1 -0.4826753 -0.914502950 FALSE
## 1958 -1 -0.4826753 -0.349805311 TRUE
## 1959 -1 -0.4826753 -0.963574269 FALSE
## 1960 -1 -0.4826753 0.604443106 TRUE
## 1961 -1 -0.4826753 -0.688774883 FALSE
## 1962 -1 -0.4826753 1.195563764 TRUE
## 1963 -1 -0.4826753 0.715419782 TRUE
## 1964 -1 -0.4826753 0.457229150 TRUE
## 1965 -1 -0.4826753 -0.804281218 FALSE
## 1966 -1 -0.4826753 -1.349350330 FALSE
## 1967 -1 -0.4826753 -0.912993063 FALSE
## 1968 -1 -0.4826753 0.993993884 TRUE
## 1969 1 -0.4826753 0.473837904 TRUE
## 1970 3 -0.4826753 1.203868141 TRUE
## 1971 -1 -0.4826753 -0.700853977 FALSE
## 1972 -1 -0.4826753 0.621806804 TRUE
## 1973 -1 -0.4826753 -0.789937294 FALSE
## 1974 -1 -0.4826753 1.994293847 TRUE
## 1975 -1 -0.4826753 -0.666126582 FALSE
## 1976 -1 -0.4826753 -0.891099706 FALSE
## 1977 -1 -0.4826753 -0.703118807 FALSE
## 1978 -1 -0.4826753 -0.708403411 FALSE
## 1979 -1 -0.4826753 0.519134506 TRUE
## 1980 -1 -0.4826753 -1.063226794 FALSE
## 1981 -1 -0.4826753 0.834700834 TRUE
## 1982 -1 -0.4826753 1.203868141 TRUE
## 1983 -1 -0.4826753 0.722214272 TRUE
## 1984 -1 -0.4826753 -0.919032610 FALSE
## 1985 -1 -0.4826753 -0.912238120 FALSE
## 1986 -1 -0.4826753 -0.857127254 FALSE
## 1987 -1 -0.4826753 1.727043895 TRUE
## 1988 -1 -0.4826753 -1.356144820 FALSE
## 1989 -1 -0.4826753 -0.897139253 FALSE
## 1990 -1 -0.4826753 -1.458062175 FALSE
## 1991 -1 -0.4826753 -0.567983944 FALSE
## 1992 -1 -0.4826753 -1.088894868 FALSE
## 1993 -1 -0.4826753 1.000788375 TRUE
## 1994 -1 -0.4826753 -0.876755782 FALSE
## 1995 -1 -0.4826753 0.261698817 TRUE
## 1996 -1 -0.4826753 -0.910728233 FALSE
## 1997 -1 -0.4826753 1.126108974 TRUE
## 1998 -1 -0.4826753 -0.700099034 FALSE
## 1999 -1 -0.4826753 -1.140985960 FALSE
## 2000 -1 -0.4826753 -0.444173232 FALSE
## 2001 -1 -0.4826753 -1.368978858 FALSE
## 2002 -1 -0.4826753 0.221686819 TRUE
## 2003 -1 -0.4826753 -1.015665361 FALSE
## 2004 -1 -0.4826753 1.915779737 TRUE
## 2005 -1 -0.4826753 -0.571003718 FALSE
## 2006 2 -0.4826753 -0.611770659 FALSE
## 2007 1 -0.4826753 1.262753723 TRUE
## 2008 -1 -0.4826753 1.770830610 TRUE
## 2009 -1 -0.4826753 -0.009325852 TRUE
## 2010 -1 -0.4826753 -0.401141460 FALSE
## 2011 -1 -0.4826753 -0.081045472 TRUE
## 2012 -1 -0.4826753 0.362106285 TRUE
## 2013 -1 -0.4826753 -0.614790433 FALSE
## 2014 1 -0.4826753 -0.177678223 FALSE
## 2015 1 -0.4826753 -0.525707116 FALSE
## 2016 3 -0.4826753 -0.726522051 FALSE
## 2017 -1 -0.4826753 -1.183262789 FALSE
## 2018 -1 -0.4826753 -0.764269220 FALSE
## 2019 -1 -0.4826753 -0.188247430 TRUE
## 2020 3 -0.4826753 1.060428901 TRUE
## 2021 -1 -0.4826753 0.854329361 TRUE
## 2022 -1 -0.4826753 -0.155029922 TRUE
## 2023 -1 -0.4826753 -1.188547393 FALSE
## 2024 -1 -0.4826753 -1.889134838 FALSE
## 2025 -1 -0.4826753 1.388074323 TRUE
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## 2027 -1 -0.4826753 0.615767257 TRUE
## 2028 -1 -0.4826753 -0.654802431 FALSE
## 2029 3 -0.4826753 1.582094768 TRUE
## 2030 -1 -0.4826753 0.515359789 TRUE
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## 2032 -1 -0.4826753 -0.090104792 TRUE
## 2033 -1 -0.4826753 1.837265627 TRUE
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## 2036 -1 -0.4826753 -0.885815102 FALSE
## 2037 -1 -0.4826753 0.274532855 TRUE
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## 2039 -1 -0.4826753 0.356821682 TRUE
## 2040 1 -0.4826753 0.211117612 TRUE
## 2041 -1 -0.4826753 -1.835533859 FALSE
## 2042 1 -0.4826753 0.130338671 FALSE
## 2043 1 -0.4826753 -0.266006597 FALSE
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## 2045 -1 -0.4826753 0.154496859 TRUE
## 2046 -1 -0.4826753 -1.019440078 FALSE
## 2047 -1 -0.4826753 -0.454742439 FALSE
## 2048 3 -0.4826753 0.744862573 TRUE
## 2049 -1 -0.4826753 0.458739036 TRUE
## 2050 2 -0.4826753 0.246599950 TRUE
## 2051 2 -0.4826753 -0.135401394 FALSE
## 2052 -1 -0.4826753 -0.389062366 FALSE
## 2053 -1 -0.4826753 -0.672921072 FALSE
## 2054 -1 -0.4826753 1.561711297 TRUE
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## 2056 -1 -0.4826753 0.923029208 TRUE
## 2057 -1 -0.4826753 -0.860901971 FALSE
## 2058 -1 -0.4826753 -1.107768452 FALSE
## 2059 3 -0.4826753 0.550842127 TRUE
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## 2108 1 -0.4826753 0.795443779 TRUE
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## 2979 -1 -0.4826753 1.253694403 TRUE
## 2980 1 -0.4826753 0.407402887 TRUE
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## 2986 3 -0.4826753 0.425521528 TRUE
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## 2999 -1 -0.4826753 1.751202083 TRUE
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## 3010 -1 -0.4826753 1.730063668 TRUE
## 3011 -1 -0.4826753 1.087606862 TRUE
## 3012 -1 -0.4826753 -1.454287458 FALSE
## 3013 1 -0.4826753 1.764791063 TRUE
## 3014 -1 -0.4826753 -0.684245223 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "externalizing_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "externalizing_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $phobias_4factorv2
## $fit
## Hydra_k3 phobias_4factorv2 visregFit visregLwr visregUpr
## 1 -1 -0.4007962 -0.3159104 -0.3535041541 -0.2783167
## 2 1 -0.4007962 0.2657206 0.1556608261 0.3757804
## 3 2 -0.4007962 0.1247434 0.0007053336 0.2487814
## 4 3 -0.4007962 0.0267178 -0.0900727445 0.1435083
##
## $res
## Hydra_k3 phobias_4factorv2 visregRes visregPos
## 1 -1 -0.4007962 -0.331180665 FALSE
## 2 -1 -0.4007962 -0.320738334 FALSE
## 3 -1 -0.4007962 1.099418669 TRUE
## 4 -1 -0.4007962 1.470121416 TRUE
## 5 -1 -0.4007962 0.561638627 TRUE
## 6 -1 -0.4007962 -0.677517974 FALSE
## 7 -1 -0.4007962 -0.858518376 FALSE
## 8 -1 -0.4007962 0.768744857 TRUE
## 9 1 -0.4007962 0.290138024 TRUE
## 10 -1 -0.4007962 -0.369469212 FALSE
## 11 3 -0.4007962 -1.551192993 FALSE
## 12 -1 -0.4007962 -0.832412549 FALSE
## 13 1 -0.4007962 1.473602193 TRUE
## 14 -1 -0.4007962 -1.418923468 FALSE
## 15 -1 -0.4007962 -0.727989240 FALSE
## 16 -1 -0.4007962 -1.264028893 FALSE
## 17 -1 -0.4007962 0.234445592 TRUE
## 18 -1 -0.4007962 -1.190932577 FALSE
## 19 -1 -0.4007962 -0.585277384 FALSE
## 20 -1 -0.4007962 0.011675866 TRUE
## 21 -1 -0.4007962 0.413705606 TRUE
## 22 -1 -0.4007962 1.292601791 TRUE
## 23 -1 -0.4007962 1.872151156 TRUE
## 24 -1 -0.4007962 -0.769758564 FALSE
## 25 1 -0.4007962 -0.106670551 FALSE
## 26 -1 -0.4007962 1.137707216 TRUE
## 27 -1 -0.4007962 0.904495159 TRUE
## 28 -1 -0.4007962 1.186438093 TRUE
## 29 -1 -0.4007962 -0.017910738 TRUE
## 30 -1 -0.4007962 -0.867220319 FALSE
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## 32 -1 -0.4007962 -1.055182275 FALSE
## 33 -1 -0.4007962 0.728715922 TRUE
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## 42 2 -0.4007962 0.942783706 TRUE
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## 62 -1 -0.4007962 0.438071045 TRUE
## 63 -1 -0.4007962 0.441551822 TRUE
## 64 -1 -0.4007962 1.922622423 TRUE
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## 67 -1 -0.4007962 0.873168166 TRUE
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## 2927 -1 -0.4007962 -1.081288102 FALSE
## 2928 3 -0.4007962 0.267512973 TRUE
## 2929 -1 -0.4007962 -0.600940881 FALSE
## 2930 -1 -0.4007962 -0.037055011 TRUE
## 2931 -1 -0.4007962 -0.144959097 TRUE
## 2932 2 -0.4007962 -1.746116504 FALSE
## 2933 1 -0.4007962 -0.989047513 FALSE
## 2934 -1 -0.4007962 -0.696662247 FALSE
## 2935 -1 -0.4007962 -0.562652334 FALSE
## 2936 -1 -0.4007962 -0.872441484 FALSE
## 2937 -1 -0.4007962 -0.127555213 TRUE
## 2938 -1 -0.4007962 -0.406017370 FALSE
## 2939 -1 -0.4007962 0.988033806 TRUE
## 2940 1 -0.4007962 0.880129720 TRUE
## 2941 -1 -0.4007962 -1.436327353 FALSE
## 2942 -1 -0.4007962 0.765264080 TRUE
## 2943 2 -0.4007962 0.834879619 TRUE
## 2944 -1 -0.4007962 -1.389336864 FALSE
## 2945 -1 -0.4007962 -0.660114089 FALSE
## 2946 -1 -0.4007962 -1.036038002 FALSE
## 2947 -1 -0.4007962 -1.408481138 FALSE
## 2948 -1 -0.4007962 -0.331180665 FALSE
## 2949 -1 -0.4007962 -0.846335657 FALSE
## 2950 1 -0.4007962 0.330166959 TRUE
## 2951 1 -0.4007962 0.486801923 TRUE
## 2952 -1 -0.4007962 -1.434586965 FALSE
## 2953 -1 -0.4007962 -0.303334450 TRUE
## 2954 -1 -0.4007962 0.648658052 TRUE
## 2955 1 -0.4007962 -0.221536191 FALSE
## 2956 -1 -0.4007962 -0.602681269 FALSE
## 2957 -1 -0.4007962 0.358013175 TRUE
## 2958 -1 -0.4007962 -0.903768477 FALSE
## 2959 -1 -0.4007962 -1.565116101 FALSE
## 2960 -1 -0.4007962 -0.999489844 FALSE
## 2961 1 -0.4007962 1.727698912 TRUE
## 2962 -1 -0.4007962 0.791369907 TRUE
## 2963 -1 -0.4007962 -0.886364592 FALSE
## 2964 2 -0.4007962 1.586727445 TRUE
## 2965 -1 -0.4007962 1.015880022 TRUE
## 2966 -1 -0.4007962 0.683465821 TRUE
## 2967 -1 -0.4007962 1.363957719 TRUE
## 2968 -1 -0.4007962 0.754821749 TRUE
## 2969 -1 -0.4007962 1.054168569 TRUE
## 2970 -1 -0.4007962 -0.867220319 FALSE
## 2971 2 -0.4007962 0.027339363 FALSE
## 2972 -1 -0.4007962 -0.613123600 FALSE
## 2973 2 -0.4007962 -0.037055011 FALSE
## 2974 -1 -0.4007962 -1.152644030 FALSE
## 2975 1 -0.4007962 2.013122624 TRUE
## 2976 -1 -0.4007962 -0.047497342 TRUE
## 2977 3 -0.4007962 4.340022028 TRUE
## 2978 3 -0.4007962 1.282159460 TRUE
## 2979 -1 -0.4007962 -0.113632105 TRUE
## 2980 1 -0.4007962 1.132486050 TRUE
## 2981 1 -0.4007962 1.196880424 TRUE
## 2982 3 -0.4007962 0.464176872 TRUE
## 2983 1 -0.4007962 2.937268909 TRUE
## 2984 -1 -0.4007962 1.598910164 TRUE
## 2985 -1 -0.4007962 1.701593085 TRUE
## 2986 3 -0.4007962 1.078534007 TRUE
## 2987 2 -0.4007962 0.981072252 TRUE
## 2988 1 -0.4007962 1.880853099 TRUE
## 2989 -1 -0.4007962 -1.027336059 FALSE
## 2990 2 -0.4007962 1.602390941 TRUE
## 2991 -1 -0.4007962 -2.052424877 FALSE
## 2992 -1 -0.4007962 -0.597460104 FALSE
## 2993 -1 -0.4007962 0.338868901 TRUE
## 2994 -1 -0.4007962 -0.964682074 FALSE
## 2995 -1 -0.4007962 -0.388613485 FALSE
## 2996 -1 -0.4007962 1.374400050 TRUE
## 2997 -1 -0.4007962 1.028062741 TRUE
## 2998 -1 -0.4007962 -0.780200894 FALSE
## 2999 -1 -0.4007962 -0.592238938 FALSE
## 3000 -1 -0.4007962 -0.924653139 FALSE
## 3001 -1 -0.4007962 0.445032599 TRUE
## 3002 -1 -0.4007962 1.623275603 TRUE
## 3003 -1 -0.4007962 -1.211817239 FALSE
## 3004 -1 -0.4007962 -1.429365799 FALSE
## 3005 2 -0.4007962 -1.845318647 FALSE
## 3006 -1 -0.4007962 -0.144959097 TRUE
## 3007 -1 -0.4007962 -0.473892521 FALSE
## 3008 -1 -0.4007962 -0.353805716 FALSE
## 3009 -1 -0.4007962 0.961927979 TRUE
## 3010 -1 -0.4007962 -0.670556420 FALSE
## 3011 -1 -0.4007962 -0.113632105 TRUE
## 3012 -1 -0.4007962 -0.198911141 TRUE
## 3013 1 -0.4007962 1.353515388 TRUE
## 3014 -1 -0.4007962 -0.842854880 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "phobias_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "phobias_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $overall_psychopathology_4factorv2
## $fit
## Hydra_k3 overall_psychopathology_4factorv2 visregFit visregLwr
## 1 -1 -0.4027408 -0.6695342 -0.7032364
## 2 1 -0.4027408 0.9975406 0.8988735
## 3 2 -0.4027408 0.8667618 0.7555634
## 4 3 -0.4027408 0.9331671 0.8284659
## visregUpr
## 1 -0.6358319
## 2 1.0962077
## 3 0.9779602
## 4 1.0378682
##
## $res
## Hydra_k3 overall_psychopathology_4factorv2 visregRes visregPos
## 1 -1 -0.4027408 0.337138191 TRUE
## 2 -1 -0.4027408 0.600484966 TRUE
## 3 -1 -0.4027408 1.450718839 TRUE
## 4 -1 -0.4027408 -1.259244878 FALSE
## 5 -1 -0.4027408 -0.563256973 TRUE
## 6 -1 -0.4027408 -0.574543263 TRUE
## 7 -1 -0.4027408 0.001057545 TRUE
## 8 -1 -0.4027408 2.017541421 TRUE
## 9 1 -0.4027408 0.816178515 FALSE
## 10 -1 -0.4027408 0.628073676 TRUE
## 11 3 -0.4027408 0.882642225 FALSE
## 12 -1 -0.4027408 -0.252256972 TRUE
## 13 1 -0.4027408 1.571105936 TRUE
## 14 -1 -0.4027408 1.105859967 TRUE
## 15 -1 -0.4027408 -0.452902134 TRUE
## 16 -1 -0.4027408 0.792351902 TRUE
## 17 -1 -0.4027408 0.221767223 TRUE
## 18 -1 -0.4027408 -0.177015036 TRUE
## 19 -1 -0.4027408 0.737174482 TRUE
## 20 -1 -0.4027408 -0.042833584 TRUE
## 21 -1 -0.4027408 -0.098011004 TRUE
## 22 -1 -0.4027408 1.147243032 TRUE
## 23 -1 -0.4027408 -0.168236810 TRUE
## 24 -1 -0.4027408 -0.140648101 TRUE
## 25 1 -0.4027408 1.016823677 TRUE
## 26 -1 -0.4027408 1.315283355 TRUE
## 27 -1 -0.4027408 0.743444644 TRUE
## 28 -1 -0.4027408 -0.135631972 TRUE
## 29 -1 -0.4027408 -0.913131974 FALSE
## 30 -1 -0.4027408 -1.118793265 FALSE
## 31 -1 -0.4027408 0.898944644 TRUE
## 32 -1 -0.4027408 -1.225386007 FALSE
## 33 -1 -0.4027408 -0.357595682 TRUE
## 34 -1 -0.4027408 0.199194642 TRUE
## 35 -1 -0.4027408 -0.264797295 TRUE
## 36 -1 -0.4027408 0.086331739 TRUE
## 37 -1 -0.4027408 -0.001450520 TRUE
## 38 2 -0.4027408 -0.291131972 FALSE
## 39 -1 -0.4027408 -0.577051328 TRUE
## 40 -1 -0.4027408 -0.799015038 FALSE
## 41 -1 -0.4027408 -0.301164230 TRUE
## 42 2 -0.4027408 0.931549483 TRUE
## 43 -1 -0.4027408 -0.708724715 FALSE
## 44 -1 -0.4027408 -0.501809392 TRUE
## 45 -1 -0.4027408 -0.175761004 TRUE
## 46 -1 -0.4027408 0.392315610 TRUE
## 47 1 -0.4027408 0.778557547 FALSE
## 48 -1 -0.4027408 -1.196543265 FALSE
## 49 -1 -0.4027408 0.121444642 TRUE
## 50 -1 -0.4027408 -0.873002941 FALSE
## 51 -1 -0.4027408 1.019331741 TRUE
## 52 3 -0.4027408 1.522198678 TRUE
## 53 -1 -0.4027408 0.022376093 TRUE
## 54 -1 -0.4027408 0.254372062 TRUE
## 55 -1 -0.4027408 -0.301164230 TRUE
## 56 -1 -0.4027408 -0.662325522 TRUE
## 57 -1 -0.4027408 -0.267305359 TRUE
## 58 -1 -0.4027408 -0.158204552 TRUE
## 59 -1 -0.4027408 0.175368029 TRUE
## 60 -1 -0.4027408 -0.338785198 TRUE
## 61 -1 -0.4027408 -0.851684393 FALSE
## 62 -1 -0.4027408 0.500162385 TRUE
## 63 -1 -0.4027408 -0.046595681 TRUE
## 64 -1 -0.4027408 -0.003958584 TRUE
## 65 -1 -0.4027408 0.093855932 TRUE
## 66 -1 -0.4027408 -0.548208586 TRUE
## 67 -1 -0.4027408 -0.521873908 TRUE
## 68 -1 -0.4027408 -1.948962622 FALSE
## 69 -1 -0.4027408 -0.382676327 TRUE
## 70 -1 -0.4027408 -0.728789231 FALSE
## 71 -1 -0.4027408 -0.567019070 TRUE
## 72 -1 -0.4027408 0.304533352 TRUE
## 73 -1 -0.4027408 -0.666087618 TRUE
## 74 -1 -0.4027408 -1.017216652 FALSE
## 75 -1 -0.4027408 -1.698156170 FALSE
## 76 -1 -0.4027408 -0.521873908 TRUE
## 77 -1 -0.4027408 0.328359965 TRUE
## 78 -1 -0.4027408 -0.128107778 TRUE
## 79 2 -0.4027408 -0.498047295 FALSE
## 80 -1 -0.4027408 2.149214809 TRUE
## 81 -1 -0.4027408 0.463795449 TRUE
## 82 -1 -0.4027408 1.678952711 TRUE
## 83 -1 -0.4027408 -0.041579552 TRUE
## 84 -1 -0.4027408 -1.339502943 FALSE
## 85 -1 -0.4027408 -0.348817456 TRUE
## 86 -1 -0.4027408 0.189162384 TRUE
## 87 1 -0.4027408 2.160501099 TRUE
## 88 -1 -0.4027408 -0.346309392 TRUE
## 89 2 -0.4027408 -0.032801326 FALSE
## 90 -1 -0.4027408 0.199194642 TRUE
## 91 -1 -0.4027408 0.954122064 TRUE
## 92 -1 -0.4027408 1.148497064 TRUE
## 93 -1 -0.4027408 0.096363997 TRUE
## 94 -1 -0.4027408 0.581674482 TRUE
## 95 2 -0.4027408 1.611234969 TRUE
## 96 -1 -0.4027408 1.028109967 TRUE
## 97 -1 -0.4027408 0.569134159 TRUE
## 98 -1 -0.4027408 0.443730933 TRUE
## 99 -1 -0.4027408 -0.062898100 TRUE
## 100 1 -0.4027408 0.290738997 FALSE
## 101 -1 -0.4027408 -1.032265039 FALSE
## 102 -1 -0.4027408 -0.103027133 TRUE
## 103 -1 -0.4027408 0.049964803 TRUE
## 104 1 -0.4027408 -0.410265037 FALSE
## 105 2 -0.4027408 0.276944642 FALSE
## 106 -1 -0.4027408 0.149033352 TRUE
## 107 -1 -0.4027408 -1.102490845 FALSE
## 108 -1 -0.4027408 -0.059136004 TRUE
## 109 -1 -0.4027408 0.915247064 TRUE
## 110 -1 -0.4027408 -0.916894071 FALSE
## 111 -1 -0.4027408 -0.931942458 FALSE
## 112 -1 -0.4027408 -0.519365844 TRUE
## 113 3 -0.4027408 0.039932545 FALSE
## 114 -1 -0.4027408 -0.644769070 TRUE
## 115 1 -0.4027408 1.297726903 TRUE
## 116 -1 -0.4027408 0.319581739 TRUE
## 117 -1 -0.4027408 1.061968838 TRUE
## 118 -1 -0.4027408 -0.583321489 TRUE
## 119 -1 -0.4027408 0.250609965 TRUE
## 120 -1 -0.4027408 -1.071140039 FALSE
## 121 -1 -0.4027408 0.308295449 TRUE
## 122 -1 -0.4027408 -0.587083586 TRUE
## 123 -1 -0.4027408 0.091347868 TRUE
## 124 -1 -0.4027408 0.165335771 TRUE
## 125 1 -0.4027408 0.804892225 FALSE
## 126 -1 -0.4027408 -0.905607780 FALSE
## 127 -1 -0.4027408 -0.732551328 FALSE
## 128 -1 -0.4027408 1.312775290 TRUE
## 129 3 -0.4027408 1.330331742 TRUE
## 130 2 -0.4027408 1.275154323 TRUE
## 131 -1 -0.4027408 0.250609965 TRUE
## 132 -1 -0.4027408 -0.657309392 TRUE
## 133 -1 -0.4027408 -1.296865846 FALSE
## 134 -1 -0.4027408 -0.587083586 TRUE
## 135 2 -0.4027408 0.046202706 FALSE
## 136 2 -0.4027408 0.228037384 FALSE
## 137 1 -0.4027408 2.026319647 TRUE
## 138 -1 -0.4027408 0.645630127 TRUE
## 139 -1 -0.4027408 -1.377123911 FALSE
## 140 -1 -0.4027408 -0.429075521 TRUE
## 141 -1 -0.4027408 -0.797761006 FALSE
## 142 -1 -0.4027408 0.791097870 TRUE
## 143 -1 -0.4027408 -0.121837617 TRUE
## 144 -1 -0.4027408 -0.202095682 TRUE
## 145 1 -0.4027408 0.146525287 FALSE
## 146 -1 -0.4027408 -0.416535198 TRUE
## 147 -1 -0.4027408 -0.205857778 TRUE
## 148 -1 -0.4027408 -1.877482783 FALSE
## 149 -1 -0.4027408 0.486368030 TRUE
## 150 -1 -0.4027408 0.392315610 TRUE
## 151 -1 -0.4027408 0.189162384 TRUE
## 152 -1 -0.4027408 0.957884160 TRUE
## 153 1 -0.4027408 0.807400289 FALSE
## 154 -1 -0.4027408 0.288230933 TRUE
## 155 -1 -0.4027408 0.946597870 TRUE
## 156 -1 -0.4027408 -0.796506974 FALSE
## 157 -1 -0.4027408 -1.417252943 FALSE
## 158 -1 -0.4027408 -0.114313423 TRUE
## 159 1 -0.4027408 -0.331261004 FALSE
## 160 -1 -0.4027408 -1.152652136 FALSE
## 161 -1 -0.4027408 0.557847869 TRUE
## 162 -1 -0.4027408 -0.244732778 TRUE
## 163 -1 -0.4027408 -1.260498910 FALSE
## 164 3 -0.4027408 0.718363998 FALSE
## 165 -1 -0.4027408 -0.920656167 FALSE
## 166 -1 -0.4027408 -0.424059392 TRUE
## 167 -1 -0.4027408 -0.085470681 TRUE
## 168 -1 -0.4027408 1.429400291 TRUE
## 169 -1 -0.4027408 -1.464906169 FALSE
## 170 3 -0.4027408 -0.358849714 FALSE
## 171 -1 -0.4027408 -1.783430363 FALSE
## 172 -1 -0.4027408 -0.161966649 TRUE
## 173 -1 -0.4027408 0.902706741 TRUE
## 174 -1 -0.4027408 0.708331740 TRUE
## 175 -1 -0.4027408 0.304533352 TRUE
## 176 -1 -0.4027408 0.856307547 TRUE
## 177 1 -0.4027408 0.744698676 FALSE
## 178 2 -0.4027408 2.225710777 TRUE
## 179 -1 -0.4027408 -0.232192456 TRUE
## 180 -1 -0.4027408 -1.679345686 FALSE
## 181 -1 -0.4027408 -1.767127944 FALSE
## 182 3 -0.4027408 0.688267224 FALSE
## 183 -1 -0.4027408 -0.963293264 FALSE
## 184 -1 -0.4027408 -0.351325521 TRUE
## 185 -1 -0.4027408 0.732158353 TRUE
## 186 -1 -0.4027408 1.319045452 TRUE
## 187 -1 -0.4027408 0.214243029 TRUE
## 188 2 -0.4027408 0.551577708 FALSE
## 189 2 -0.4027408 -0.089232778 FALSE
## 190 -1 -0.4027408 -0.378914230 TRUE
## 191 -1 -0.4027408 -0.372644069 TRUE
## 192 1 -0.4027408 1.864549486 TRUE
## 193 -1 -0.4027408 0.597976901 TRUE
## 194 -1 -0.4027408 -0.663579554 TRUE
## 195 -1 -0.4027408 -0.449140037 TRUE
## 196 3 -0.4027408 2.007509163 TRUE
## 197 -1 -0.4027408 0.998013193 TRUE
## 198 -1 -0.4027408 1.479561581 TRUE
## 199 3 -0.4027408 0.581674482 FALSE
## 200 2 -0.4027408 1.198658355 TRUE
## 201 -1 -0.4027408 -0.761394070 FALSE
## 202 2 -0.4027408 1.673936582 TRUE
## 203 1 -0.4027408 1.464513194 TRUE
## 204 -1 -0.4027408 -0.589591650 TRUE
## 205 -1 -0.4027408 0.883896257 TRUE
## 206 -1 -0.4027408 0.649392224 TRUE
## 207 -1 -0.4027408 -0.521873908 TRUE
## 208 -1 -0.4027408 -0.278591649 TRUE
## 209 -1 -0.4027408 0.597976901 TRUE
## 210 3 -0.4027408 -0.283607779 FALSE
## 211 -1 -0.4027408 1.319045452 TRUE
## 212 3 -0.4027408 1.341618032 TRUE
## 213 -1 -0.4027408 -0.860462619 FALSE
## 214 -1 -0.4027408 0.423666417 TRUE
## 215 -1 -0.4027408 -0.214636004 TRUE
## 216 -1 -0.4027408 -1.285579556 FALSE
## 217 -1 -0.4027408 -1.324454556 FALSE
## 218 -1 -0.4027408 -1.340756975 FALSE
## 219 -1 -0.4027408 0.228037384 TRUE
## 220 -1 -0.4027408 0.769779321 TRUE
## 221 -1 -0.4027408 0.490130127 TRUE
## 222 -1 -0.4027408 -1.181494878 FALSE
## 223 -1 -0.4027408 -0.847922296 FALSE
## 224 1 -0.4027408 1.796831743 TRUE
## 225 -1 -0.4027408 1.820658356 TRUE
## 226 -1 -0.4027408 -1.710696492 FALSE
## 227 1 -0.4027408 0.945343838 FALSE
## 228 1 -0.4027408 0.128968836 FALSE
## 229 -1 -0.4027408 -1.130079555 FALSE
## 230 1 -0.4027408 1.015569644 TRUE
## 231 -1 -0.4027408 -0.682390038 FALSE
## 232 3 -0.4027408 1.078271258 TRUE
## 233 1 -0.4027408 1.302743032 TRUE
## 234 2 -0.4027408 0.733412386 FALSE
## 235 -1 -0.4027408 0.781065612 TRUE
## 236 -1 -0.4027408 -0.386438424 TRUE
## 237 -1 -0.4027408 -0.837890038 FALSE
## 238 2 -0.4027408 0.372251094 FALSE
## 239 -1 -0.4027408 -0.391454553 TRUE
## 240 -1 -0.4027408 0.189162384 TRUE
## 241 2 -0.4027408 1.201166419 TRUE
## 242 -1 -0.4027408 -0.232192456 TRUE
## 243 -1 -0.4027408 -0.628466650 TRUE
## 244 -1 -0.4027408 -0.034055358 TRUE
## 245 -1 -0.4027408 -0.292386004 TRUE
## 246 -1 -0.4027408 -1.659281169 FALSE
## 247 -1 -0.4027408 -1.567736814 FALSE
## 248 -1 -0.4027408 -0.150680359 TRUE
## 249 -1 -0.4027408 -1.261752943 FALSE
## 250 -1 -0.4027408 0.920263193 TRUE
## 251 -1 -0.4027408 -0.809047296 FALSE
## 252 -1 -0.4027408 0.260642223 TRUE
## 253 -1 -0.4027408 -0.906861813 FALSE
## 254 -1 -0.4027408 0.181638191 TRUE
## 255 1 -0.4027408 0.171605932 FALSE
## 256 -1 -0.4027408 -1.102490845 FALSE
## 257 -1 -0.4027408 -1.538894072 FALSE
## 258 -1 -0.4027408 -0.608402134 TRUE
## 259 3 -0.4027408 0.940327709 TRUE
## 260 3 -0.4027408 -0.174506972 FALSE
## 261 -1 -0.4027408 -0.120583585 TRUE
## 262 -1 -0.4027408 -0.215890036 TRUE
## 263 -1 -0.4027408 1.354158355 TRUE
## 264 -1 -0.4027408 -1.051075523 FALSE
## 265 -1 -0.4027408 0.541545450 TRUE
## 266 -1 -0.4027408 -0.829111812 FALSE
## 267 3 -0.4027408 -0.037817455 FALSE
## 268 3 -0.4027408 1.503388194 TRUE
## 269 2 -0.4027408 0.674472869 FALSE
## 270 -1 -0.4027408 -0.790236812 FALSE
## 271 2 -0.4027408 -0.010228746 FALSE
## 272 3 -0.4027408 0.653154321 FALSE
## 273 -1 -0.4027408 -0.699946489 FALSE
## 274 -1 -0.4027408 0.971678515 TRUE
## 275 -1 -0.4027408 -0.666087618 TRUE
## 276 -1 -0.4027408 -1.572752943 FALSE
## 277 -1 -0.4027408 -0.005212616 TRUE
## 278 -1 -0.4027408 1.598694646 TRUE
## 279 -1 -0.4027408 -0.510587618 TRUE
## 280 -1 -0.4027408 -0.788982780 FALSE
## 281 -1 -0.4027408 -1.316930362 FALSE
## 282 -1 -0.4027408 -0.604640037 TRUE
## 283 2 -0.4027408 1.464513194 TRUE
## 284 -1 -0.4027408 -0.200841649 TRUE
## 285 -1 -0.4027408 0.703315611 TRUE
## 286 -1 -0.4027408 0.376013191 TRUE
## 287 -1 -0.4027408 -1.908833590 FALSE
## 288 1 -0.4027408 0.817432547 FALSE
## 289 1 -0.4027408 1.598694646 TRUE
## 290 -1 -0.4027408 -0.641006973 TRUE
## 291 -1 -0.4027408 0.271928513 TRUE
## 292 -1 -0.4027408 -0.348817456 TRUE
## 293 -1 -0.4027408 -1.861180364 FALSE
## 294 2 -0.4027408 2.175549486 TRUE
## 295 -1 -0.4027408 -0.822841651 FALSE
## 296 2 -0.4027408 2.392497068 TRUE
## 297 -1 -0.4027408 -0.824095683 FALSE
## 298 -1 -0.4027408 0.470065611 TRUE
## 299 -1 -0.4027408 -1.553942459 FALSE
## 300 1 -0.4027408 1.609980936 TRUE
## 301 3 -0.4027408 0.309549481 FALSE
## 302 -1 -0.4027408 0.318327707 TRUE
## 303 -1 -0.4027408 0.218005126 TRUE
## 304 -1 -0.4027408 -0.392708585 TRUE
## 305 1 -0.4027408 0.421158352 FALSE
## 306 -1 -0.4027408 -1.156414233 FALSE
## 307 -1 -0.4027408 1.014315612 TRUE
## 308 -1 -0.4027408 -0.896829554 FALSE
## 309 -1 -0.4027408 -0.154442456 TRUE
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## 1946 -1 -0.4027408 -1.709442460 FALSE
## 1947 -1 -0.4027408 -0.614672296 TRUE
## 1948 -1 -0.4027408 -1.412236814 FALSE
## 1949 -1 -0.4027408 -1.987837622 FALSE
## 1950 -1 -0.4027408 -1.856164235 FALSE
## 1951 -1 -0.4027408 -1.673075524 FALSE
## 1952 -1 -0.4027408 -1.252974717 FALSE
## 1953 -1 -0.4027408 -1.678091653 FALSE
## 1954 -1 -0.4027408 -0.490523102 TRUE
## 1955 -1 -0.4027408 -0.019006971 TRUE
## 1956 -1 -0.4027408 -0.726281167 FALSE
## 1957 -1 -0.4027408 -1.247958588 FALSE
## 1958 -1 -0.4027408 -0.964547297 FALSE
## 1959 -1 -0.4027408 -1.041043265 FALSE
## 1960 -1 -0.4027408 -1.548926330 FALSE
## 1961 -1 -0.4027408 -1.892531170 FALSE
## 1962 -1 -0.4027408 0.275690610 TRUE
## 1963 -1 -0.4027408 -0.179523101 TRUE
## 1964 -1 -0.4027408 -0.704962618 FALSE
## 1965 -1 -0.4027408 -1.670567460 FALSE
## 1966 -1 -0.4027408 -0.287369875 TRUE
## 1967 -1 -0.4027408 0.374759159 TRUE
## 1968 -1 -0.4027408 -0.941974716 FALSE
## 1969 1 -0.4027408 -0.069168262 FALSE
## 1970 3 -0.4027408 1.474545452 TRUE
## 1971 -1 -0.4027408 -1.911341654 FALSE
## 1972 -1 -0.4027408 -0.886797296 FALSE
## 1973 -1 -0.4027408 -1.501273105 FALSE
## 1974 -1 -0.4027408 -1.481208588 FALSE
## 1975 -1 -0.4027408 -1.915103751 FALSE
## 1976 -1 -0.4027408 -1.261752943 FALSE
## 1977 -1 -0.4027408 -1.852402138 FALSE
## 1978 -1 -0.4027408 -1.780922299 FALSE
## 1979 -1 -0.4027408 -0.896829554 FALSE
## 1980 -1 -0.4027408 -0.866732780 FALSE
## 1981 -1 -0.4027408 -1.916357783 FALSE
## 1982 -1 -0.4027408 -1.120047297 FALSE
## 1983 -1 -0.4027408 -1.354551330 FALSE
## 1984 -1 -0.4027408 -1.400950524 FALSE
## 1985 -1 -0.4027408 -1.345773104 FALSE
## 1986 -1 -0.4027408 -1.316930362 FALSE
## 1987 -1 -0.4027408 -1.363329556 FALSE
## 1988 -1 -0.4027408 -0.324990843 TRUE
## 1989 -1 -0.4027408 -1.311914233 FALSE
## 1990 -1 -0.4027408 -0.195825520 TRUE
## 1991 -1 -0.4027408 -1.898801331 FALSE
## 1992 -1 -0.4027408 -0.904353748 FALSE
## 1993 -1 -0.4027408 -0.962039232 FALSE
## 1994 -1 -0.4027408 -1.000914232 FALSE
## 1995 -1 -0.4027408 -0.785220683 FALSE
## 1996 -1 -0.4027408 -1.165192458 FALSE
## 1997 -1 -0.4027408 0.036170448 TRUE
## 1998 -1 -0.4027408 -1.634200524 FALSE
## 1999 -1 -0.4027408 -0.629720683 TRUE
## 2000 -1 -0.4027408 -0.763902135 FALSE
## 2001 -1 -0.4027408 -0.298656166 TRUE
## 2002 -1 -0.4027408 -0.643515038 TRUE
## 2003 -1 -0.4027408 -1.211591652 FALSE
## 2004 -1 -0.4027408 -0.292386004 TRUE
## 2005 -1 -0.4027408 -1.907579557 FALSE
## 2006 2 -0.4027408 -0.039071488 FALSE
## 2007 1 -0.4027408 1.411843839 TRUE
## 2008 -1 -0.4027408 -0.990881974 FALSE
## 2009 -1 -0.4027408 -1.249212620 FALSE
## 2010 -1 -0.4027408 0.243085771 TRUE
## 2011 -1 -0.4027408 -0.694930360 FALSE
## 2012 -1 -0.4027408 -1.096220684 FALSE
## 2013 -1 -0.4027408 -0.190809391 TRUE
## 2014 1 -0.4027408 1.004283354 TRUE
## 2015 1 -0.4027408 -0.436599714 FALSE
## 2016 3 -0.4027408 0.572896256 FALSE
## 2017 -1 -0.4027408 -0.778950522 FALSE
## 2018 -1 -0.4027408 -1.715712621 FALSE
## 2019 -1 -0.4027408 -0.168236810 TRUE
## 2020 3 -0.4027408 0.737174482 FALSE
## 2021 -1 -0.4027408 -1.073648103 FALSE
## 2022 -1 -0.4027408 -1.069886007 FALSE
## 2023 -1 -0.4027408 -0.697438425 FALSE
## 2024 -1 -0.4027408 1.381747065 TRUE
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## 2026 -1 -0.4027408 0.991743031 TRUE
## 2027 -1 -0.4027408 0.285722868 TRUE
## 2028 -1 -0.4027408 0.776049483 TRUE
## 2029 3 -0.4027408 1.404319646 TRUE
## 2030 -1 -0.4027408 -0.071676326 TRUE
## 2031 -1 -0.4027408 -0.953261006 FALSE
## 2032 -1 -0.4027408 -0.455410198 TRUE
## 2033 -1 -0.4027408 -0.698692457 FALSE
## 2034 -1 -0.4027408 -1.421015040 FALSE
## 2035 -1 -0.4027408 0.471319643 TRUE
## 2036 -1 -0.4027408 -1.150144071 FALSE
## 2037 -1 -0.4027408 -0.652293263 TRUE
## 2038 -1 -0.4027408 0.117682545 TRUE
## 2039 -1 -0.4027408 -0.896829554 FALSE
## 2040 1 -0.4027408 1.041904322 TRUE
## 2041 -1 -0.4027408 0.490130127 TRUE
## 2042 1 -0.4027408 1.949823679 TRUE
## 2043 1 -0.4027408 1.193642226 TRUE
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## 2046 -1 -0.4027408 -1.028502942 FALSE
## 2047 -1 -0.4027408 -0.022769068 TRUE
## 2048 3 -0.4027408 1.329077710 TRUE
## 2049 -1 -0.4027408 0.179130126 TRUE
## 2050 2 -0.4027408 1.841976905 TRUE
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## 2052 -1 -0.4027408 -0.156950520 TRUE
## 2053 -1 -0.4027408 -1.844877944 FALSE
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## 2057 -1 -0.4027408 1.013061580 TRUE
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## 2059 3 -0.4027408 1.199912387 TRUE
## 2060 3 -0.4027408 -0.362611811 FALSE
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## 2067 1 -0.4027408 0.841259160 FALSE
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## 2071 -1 -0.4027408 -1.360821491 FALSE
## 2072 -1 -0.4027408 0.373505127 TRUE
## 2073 3 -0.4027408 -0.228430359 FALSE
## 2074 -1 -0.4027408 -1.252974717 FALSE
## 2075 -1 -0.4027408 -1.862434396 FALSE
## 2076 1 -0.4027408 0.095109964 FALSE
## 2077 2 -0.4027408 1.260105935 TRUE
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## 2080 -1 -0.4027408 -0.228430359 TRUE
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## 2086 -1 -0.4027408 0.234307546 TRUE
## 2087 -1 -0.4027408 -2.024204558 FALSE
## 2088 -1 -0.4027408 -0.555732779 TRUE
## 2089 -1 -0.4027408 0.556593837 TRUE
## 2090 -1 -0.4027408 0.202956739 TRUE
## 2091 1 -0.4027408 1.028109967 TRUE
## 2092 -1 -0.4027408 -1.097474716 FALSE
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## 2095 -1 -0.4027408 0.812416418 TRUE
## 2096 3 -0.4027408 0.813670450 FALSE
## 2097 -1 -0.4027408 -1.204067459 FALSE
## 2098 -1 -0.4027408 -0.577051328 TRUE
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## 2105 2 -0.4027408 -0.062898100 FALSE
## 2106 1 -0.4027408 1.803101905 TRUE
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## 2108 1 -0.4027408 1.534739001 TRUE
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## 2118 1 -0.4027408 0.941581741 FALSE
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## 2120 -1 -0.4027408 1.534739001 TRUE
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## 2122 -1 -0.4027408 -0.619688425 TRUE
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## 2235 -1 -0.4027408 -0.398978747 TRUE
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## 2748 -1 -0.4027408 -1.999123912 FALSE
## 2749 -1 -0.4027408 -1.200305362 FALSE
## 2750 2 -0.4027408 0.194178513 FALSE
## 2751 -1 -0.4027408 -1.950216654 FALSE
## 2752 -1 -0.4027408 -1.513813427 FALSE
## 2753 -1 -0.4027408 -1.190273104 FALSE
## 2754 -1 -0.4027408 -1.505035201 FALSE
## 2755 -1 -0.4027408 -1.887515041 FALSE
## 2756 2 -0.4027408 0.350932546 FALSE
## 2757 -1 -0.4027408 -1.972789235 FALSE
## 2758 -1 -0.4027408 -1.970281170 FALSE
## 2759 3 -0.4027408 1.691493033 TRUE
## 2760 -1 -0.4027408 0.049964803 TRUE
## 2761 -1 -0.4027408 -1.726998912 FALSE
## 2762 2 -0.4027408 0.811162386 FALSE
## 2763 -1 -0.4027408 -0.121837617 TRUE
## 2764 -1 -0.4027408 -0.987119877 FALSE
## 2765 1 -0.4027408 1.579884162 TRUE
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## 2767 -1 -0.4027408 -1.173970684 FALSE
## 2768 -1 -0.4027408 -0.886797296 FALSE
## 2769 -1 -0.4027408 -1.849894073 FALSE
## 2770 -1 -0.4027408 -1.072394071 FALSE
## 2771 -1 -0.4027408 0.318327707 TRUE
## 2772 -1 -0.4027408 -0.242224714 TRUE
## 2773 -1 -0.4027408 -1.103744878 FALSE
## 2774 1 -0.4027408 1.415605936 TRUE
## 2775 -1 -0.4027408 -0.203349714 TRUE
## 2776 -1 -0.4027408 -1.816035202 FALSE
## 2777 -1 -0.4027408 -1.468668266 FALSE
## 2778 -1 -0.4027408 -0.999660200 FALSE
## 2779 -1 -0.4027408 -1.885006977 FALSE
## 2780 -1 -0.4027408 -1.141365845 FALSE
## 2781 -1 -0.4027408 -1.310660201 FALSE
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## 2783 -1 -0.4027408 -1.561466653 FALSE
## 2784 -1 -0.4027408 -1.883752944 FALSE
## 2785 -1 -0.4027408 -1.123809394 FALSE
## 2786 -1 -0.4027408 -1.768381976 FALSE
## 2787 1 -0.4027408 -0.268559391 FALSE
## 2788 -1 -0.4027408 -1.022232781 FALSE
## 2789 -1 -0.4027408 -1.084934394 FALSE
## 2790 -1 -0.4027408 -1.439825524 FALSE
## 2791 -1 -0.4027408 -0.388946488 TRUE
## 2792 -1 -0.4027408 -1.812273105 FALSE
## 2793 -1 -0.4027408 -1.974043267 FALSE
## 2794 -1 -0.4027408 -1.221623910 FALSE
## 2795 -1 -0.4027408 -1.750825525 FALSE
## 2796 -1 -0.4027408 -0.565765037 TRUE
## 2797 -1 -0.4027408 -1.900055364 FALSE
## 2798 -1 -0.4027408 1.270138194 TRUE
## 2799 -1 -0.4027408 -2.002886009 FALSE
## 2800 3 -0.4027408 -0.192063423 FALSE
## 2801 -1 -0.4027408 -1.133841652 FALSE
## 2802 2 -0.4027408 0.133984965 FALSE
## 2803 -1 -0.4027408 -0.431583585 TRUE
## 2804 -1 -0.4027408 -0.619688425 TRUE
## 2805 -1 -0.4027408 -1.632946492 FALSE
## 2806 -1 -0.4027408 -1.325708588 FALSE
## 2807 -1 -0.4027408 -1.478700524 FALSE
## 2808 -1 -0.4027408 -1.764619879 FALSE
## 2809 -1 -0.4027408 -1.387156169 FALSE
## 2810 -1 -0.4027408 -1.037281168 FALSE
## 2811 -1 -0.4027408 -1.619152137 FALSE
## 2812 -1 -0.4027408 -0.579559392 TRUE
## 2813 3 -0.4027408 1.720335775 TRUE
## 2814 -1 -0.4027408 -1.640470686 FALSE
## 2815 -1 -0.4027408 -1.419761008 FALSE
## 2816 -1 -0.4027408 -2.016680364 FALSE
## 2817 -1 -0.4027408 0.956630128 TRUE
## 2818 -1 -0.4027408 0.128968836 TRUE
## 2819 -1 -0.4027408 -1.827321493 FALSE
## 2820 -1 -0.4027408 -1.975297299 FALSE
## 2821 -1 -0.4027408 -1.738285202 FALSE
## 2822 1 -0.4027408 2.264585777 TRUE
## 2823 -1 -0.4027408 -1.370853749 FALSE
## 2824 -1 -0.4027408 -1.310660201 FALSE
## 2825 -1 -0.4027408 0.075045448 TRUE
## 2826 1 -0.4027408 1.344126097 TRUE
## 2827 2 -0.4027408 -0.237208585 FALSE
## 2828 -1 -0.4027408 0.397331740 TRUE
## 2829 1 -0.4027408 1.949823679 TRUE
## 2830 -1 -0.4027408 0.628073676 TRUE
## 2831 -1 -0.4027408 -1.649248911 FALSE
## 2832 -1 -0.4027408 -0.708724715 FALSE
## 2833 -1 -0.4027408 -1.896293267 FALSE
## 2834 3 -0.4027408 0.165335771 FALSE
## 2835 -1 -0.4027408 -1.170208587 FALSE
## 2836 -1 -0.4027408 -1.794716654 FALSE
## 2837 -1 -0.4027408 -1.917611815 FALSE
## 2838 -1 -0.4027408 -0.287369875 TRUE
## 2839 -1 -0.4027408 0.447493030 TRUE
## 2840 -1 -0.4027408 -0.480490844 TRUE
## 2841 -1 -0.4027408 -1.015962619 FALSE
## 2842 -1 -0.4027408 -0.780204554 FALSE
## 2843 -1 -0.4027408 -0.116821488 TRUE
## 2844 -1 -0.4027408 -1.216607781 FALSE
## 2845 -1 -0.4027408 -0.545700521 TRUE
## 2846 -1 -0.4027408 -0.490523102 TRUE
## 2847 -1 -0.4027408 -1.916357783 FALSE
## 2848 1 -0.4027408 1.104605935 TRUE
## 2849 -1 -0.4027408 -1.318184394 FALSE
## 2850 1 -0.4027408 0.337138191 FALSE
## 2851 -1 -0.4027408 -1.898801331 FALSE
## 2852 -1 -0.4027408 -1.118793265 FALSE
## 2853 -1 -0.4027408 -1.681853750 FALSE
## 2854 1 -0.4027408 1.148497064 TRUE
## 2855 -1 -0.4027408 -1.827321493 FALSE
## 2856 -1 -0.4027408 -1.493748911 FALSE
## 2857 1 -0.4027408 0.833734967 FALSE
## 2858 3 -0.4027408 0.432444643 FALSE
## 2859 -1 -0.4027408 -1.713204557 FALSE
## 2860 -1 -0.4027408 -1.200305362 FALSE
## 2861 -1 -0.4027408 -0.687406167 FALSE
## 2862 -1 -0.4027408 -1.138857781 FALSE
## 2863 -1 -0.4027408 -1.637962621 FALSE
## 2864 3 -0.4027408 1.468275291 TRUE
## 2865 -1 -0.4027408 -1.948962622 FALSE
## 2866 -1 -0.4027408 -1.298119878 FALSE
## 2867 -1 -0.4027408 -0.504317457 TRUE
## 2868 3 -0.4027408 -0.282353746 FALSE
## 2869 1 -0.4027408 2.159247067 TRUE
## 2870 -1 -0.4027408 -1.369599717 FALSE
## 2871 -1 -0.4027408 -0.326244875 TRUE
## 2872 -1 -0.4027408 -0.317466650 TRUE
## 2873 -1 -0.4027408 -1.931406170 FALSE
## 2874 -1 -0.4027408 -0.967055361 FALSE
## 2875 -1 -0.4027408 -0.047849713 TRUE
## 2876 -1 -0.4027408 -0.296148101 TRUE
## 2877 -1 -0.4027408 -0.555732779 TRUE
## 2878 -1 -0.4027408 -1.324454556 FALSE
## 2879 -1 -0.4027408 -0.184539230 TRUE
## 2880 -1 -0.4027408 0.101380126 TRUE
## 2881 -1 -0.4027408 -1.946454557 FALSE
## 2882 -1 -0.4027408 -1.698156170 FALSE
## 2883 -1 -0.4027408 -1.482462621 FALSE
## 2884 -1 -0.4027408 0.901452709 TRUE
## 2885 -1 -0.4027408 -0.370136005 TRUE
## 2886 -1 -0.4027408 -1.145127942 FALSE
## 2887 -1 -0.4027408 -0.582067457 TRUE
## 2888 -1 -0.4027408 1.241295452 TRUE
## 2889 -1 -0.4027408 -0.316212617 TRUE
## 2890 -1 -0.4027408 0.472573675 TRUE
## 2891 1 -0.4027408 1.597440614 TRUE
## 2892 -1 -0.4027408 0.433698675 TRUE
## 2893 3 -0.4027408 0.705823676 FALSE
## 2894 -1 -0.4027408 -1.946454557 FALSE
## 2895 -1 -0.4027408 -1.733269073 FALSE
## 2896 -1 -0.4027408 -0.619688425 TRUE
## 2897 -1 -0.4027408 -1.468668266 FALSE
## 2898 -1 -0.4027408 1.251327710 TRUE
## 2899 -1 -0.4027408 -1.094966652 FALSE
## 2900 2 -0.4027408 1.720335775 TRUE
## 2901 3 -0.4027408 1.118400290 TRUE
## 2902 -1 -0.4027408 -0.129361810 TRUE
## 2903 -1 -0.4027408 -1.200305362 FALSE
## 2904 -1 -0.4027408 -1.681853750 FALSE
## 2905 -1 -0.4027408 -0.271067456 TRUE
## 2906 2 -0.4027408 0.532767224 FALSE
## 2907 -1 -0.4027408 -0.440361811 TRUE
## 2908 -1 -0.4027408 -0.694930360 FALSE
## 2909 -1 -0.4027408 0.989234967 TRUE
## 2910 -1 -0.4027408 -1.333232781 FALSE
## 2911 -1 -0.4027408 -1.457381975 FALSE
## 2912 1 -0.4027408 0.280706739 FALSE
## 2913 -1 -0.4027408 -0.567019070 TRUE
## 2914 -1 -0.4027408 -1.178986813 FALSE
## 2915 -1 -0.4027408 -0.410265037 TRUE
## 2916 -1 -0.4027408 -1.251720684 FALSE
## 2917 2 -0.4027408 1.198658355 TRUE
## 2918 2 -0.4027408 1.238787387 TRUE
## 2919 -1 -0.4027408 -0.898083587 FALSE
## 2920 -1 -0.4027408 -0.238462617 TRUE
## 2921 -1 -0.4027408 -1.338248911 FALSE
## 2922 -1 -0.4027408 -0.615926328 TRUE
## 2923 -1 -0.4027408 -0.066660197 TRUE
## 2924 -1 -0.4027408 -2.058063429 FALSE
## 2925 -1 -0.4027408 -1.254228749 FALSE
## 2926 -1 -0.4027408 -0.658563425 TRUE
## 2927 -1 -0.4027408 -0.337531166 TRUE
## 2928 3 -0.4027408 0.393569643 FALSE
## 2929 -1 -0.4027408 -0.269813424 TRUE
## 2930 -1 -0.4027408 0.036170448 TRUE
## 2931 -1 -0.4027408 -0.916894071 FALSE
## 2932 2 -0.4027408 0.360964804 FALSE
## 2933 1 -0.4027408 0.088839803 FALSE
## 2934 -1 -0.4027408 -2.009156170 FALSE
## 2935 -1 -0.4027408 -0.488015037 TRUE
## 2936 -1 -0.4027408 -1.856164235 FALSE
## 2937 -1 -0.4027408 -1.224131975 FALSE
## 2938 -1 -0.4027408 -0.283607779 TRUE
## 2939 -1 -0.4027408 -0.646023102 TRUE
## 2940 1 -0.4027408 1.528468839 TRUE
## 2941 -1 -0.4027408 0.488876095 TRUE
## 2942 -1 -0.4027408 -0.094248907 TRUE
## 2943 2 -0.4027408 0.281960771 FALSE
## 2944 -1 -0.4027408 -0.277337617 TRUE
## 2945 -1 -0.4027408 -1.113777136 FALSE
## 2946 -1 -0.4027408 -0.302418262 TRUE
## 2947 -1 -0.4027408 0.472573675 TRUE
## 2948 -1 -0.4027408 -0.158204552 TRUE
## 2949 -1 -0.4027408 -1.807256976 FALSE
## 2950 1 -0.4027408 1.209944645 TRUE
## 2951 1 -0.4027408 1.487085775 TRUE
## 2952 -1 -0.4027408 -0.407756972 TRUE
## 2953 -1 -0.4027408 -0.941974716 FALSE
## 2954 -1 -0.4027408 -0.979595684 FALSE
## 2955 1 -0.4027408 2.110339809 TRUE
## 2956 -1 -0.4027408 0.986726902 TRUE
## 2957 -1 -0.4027408 -1.252974717 FALSE
## 2958 -1 -0.4027408 -1.739539234 FALSE
## 2959 -1 -0.4027408 0.434952707 TRUE
## 2960 -1 -0.4027408 0.053726900 TRUE
## 2961 1 -0.4027408 1.441940613 TRUE
## 2962 -1 -0.4027408 -0.251002940 TRUE
## 2963 -1 -0.4027408 -1.928898106 FALSE
## 2964 2 -0.4027408 1.836960776 TRUE
## 2965 -1 -0.4027408 -0.773934393 FALSE
## 2966 -1 -0.4027408 -0.328752940 TRUE
## 2967 -1 -0.4027408 -0.629720683 TRUE
## 2968 -1 -0.4027408 0.388553514 TRUE
## 2969 -1 -0.4027408 -1.589055363 FALSE
## 2970 -1 -0.4027408 -0.751361812 FALSE
## 2971 2 -0.4027408 0.829972870 FALSE
## 2972 -1 -0.4027408 -0.376406166 TRUE
## 2973 2 -0.4027408 -0.213381972 FALSE
## 2974 -1 -0.4027408 -1.092458587 FALSE
## 2975 1 -0.4027408 1.591170453 TRUE
## 2976 -1 -0.4027408 1.148497064 TRUE
## 2977 3 -0.4027408 0.190416416 FALSE
## 2978 3 -0.4027408 0.620549482 FALSE
## 2979 -1 -0.4027408 -0.609656167 TRUE
## 2980 1 -0.4027408 0.735920450 FALSE
## 2981 1 -0.4027408 0.748460773 FALSE
## 2982 3 -0.4027408 0.962900289 TRUE
## 2983 1 -0.4027408 0.732158353 FALSE
## 2984 -1 -0.4027408 -0.672357780 FALSE
## 2985 -1 -0.4027408 0.164081739 TRUE
## 2986 3 -0.4027408 0.394823675 FALSE
## 2987 2 -0.4027408 0.249355933 FALSE
## 2988 1 -0.4027408 0.605501095 FALSE
## 2989 -1 -0.4027408 0.266912384 TRUE
## 2990 2 -0.4027408 0.329613997 FALSE
## 2991 -1 -0.4027408 0.860069644 TRUE
## 2992 -1 -0.4027408 -0.598369876 TRUE
## 2993 -1 -0.4027408 -1.513813427 FALSE
## 2994 -1 -0.4027408 -1.357059394 FALSE
## 2995 -1 -0.4027408 0.841259160 TRUE
## 2996 -1 -0.4027408 0.606755127 TRUE
## 2997 -1 -0.4027408 -1.056091652 FALSE
## 2998 -1 -0.4027408 -1.841115847 FALSE
## 2999 -1 -0.4027408 -0.140648101 TRUE
## 3000 -1 -0.4027408 -0.019006971 TRUE
## 3001 -1 -0.4027408 0.224275287 TRUE
## 3002 -1 -0.4027408 0.149033352 TRUE
## 3003 -1 -0.4027408 -0.812809393 FALSE
## 3004 -1 -0.4027408 -0.225922294 TRUE
## 3005 2 -0.4027408 2.214424487 TRUE
## 3006 -1 -0.4027408 -1.701918266 FALSE
## 3007 -1 -0.4027408 -0.368881972 TRUE
## 3008 -1 -0.4027408 -0.972071490 FALSE
## 3009 -1 -0.4027408 0.047456739 TRUE
## 3010 -1 -0.4027408 0.026138190 TRUE
## 3011 -1 -0.4027408 -1.190273104 FALSE
## 3012 -1 -0.4027408 -0.189555359 TRUE
## 3013 1 -0.4027408 0.537783353 FALSE
## 3014 -1 -0.4027408 -1.877482783 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "overall_psychopathology_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "overall_psychopathology_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"lapply(clinical_cog_score_cluster_stats_lm_AG_resid, function(x) {visreg(x)}) 
## $mood_4factorv2
## $fit
## Hydra_k3 mood_4factorv2 visregFit visregLwr visregUpr
## 1 -1 -0.1240654 -0.2685618 -0.3043972 -0.2327264
## 2 1 -0.1240654 0.9790795 0.8706729 1.0874861
## 3 2 -0.1240654 1.1122240 0.9979787 1.2264692
## 4 3 -0.1240654 0.9844508 0.8735878 1.0953139
##
## $res
## Hydra_k3 mood_4factorv2 visregRes visregPos
## 1 -1 -0.1240654 0.409515203 TRUE
## 2 -1 -0.1240654 0.358555255 TRUE
## 3 -1 -0.1240654 1.467683543 TRUE
## 4 -1 -0.1240654 -0.839003532 FALSE
## 5 -1 -0.1240654 -0.121067789 TRUE
## 6 -1 -0.1240654 0.433496355 TRUE
## 7 -1 -0.1240654 0.436493999 TRUE
## 8 -1 -0.1240654 -0.653149603 FALSE
## 9 3 -0.1240654 0.357056433 FALSE
## 10 -1 -0.1240654 1.152930921 TRUE
## 11 2 -0.1240654 2.573814188 TRUE
## 12 -1 -0.1240654 0.820192434 TRUE
## 13 3 -0.1240654 0.394526983 FALSE
## 14 -1 -0.1240654 -1.101297384 FALSE
## 15 -1 -0.1240654 -1.012866886 FALSE
## 16 -1 -0.1240654 -1.288650136 FALSE
## 17 -1 -0.1240654 0.255136536 TRUE
## 18 -1 -0.1240654 1.088481574 TRUE
## 19 -1 -0.1240654 -1.481998175 FALSE
## 20 -1 -0.1240654 0.503940990 TRUE
## 21 -1 -0.1240654 1.403234197 TRUE
## 22 -1 -0.1240654 0.695790207 TRUE
## 23 -1 -0.1240654 0.547406828 TRUE
## 24 -1 -0.1240654 0.985062856 TRUE
## 25 3 -0.1240654 0.144223707 FALSE
## 26 -1 -0.1240654 1.064500422 TRUE
## 27 -1 -0.1240654 2.979994953 TRUE
## 28 -1 -0.1240654 1.741967971 TRUE
## 29 -1 -0.1240654 0.351061145 TRUE
## 30 -1 -0.1240654 -0.877972905 FALSE
## 31 -1 -0.1240654 2.979994953 TRUE
## 32 -1 -0.1240654 -0.897457591 FALSE
## 33 -1 -0.1240654 -1.194224349 FALSE
## 34 -1 -0.1240654 0.307595306 TRUE
## 35 -1 -0.1240654 2.326508556 TRUE
## 36 -1 -0.1240654 -1.520967547 FALSE
## 37 -1 -0.1240654 0.277618866 TRUE
## 38 2 -0.1240654 2.636764712 TRUE
## 39 -1 -0.1240654 0.724267826 TRUE
## 40 -1 -0.1240654 -0.653149603 FALSE
## 41 -1 -0.1240654 1.163422675 TRUE
## 42 1 -0.1240654 2.941025580 TRUE
## 43 -1 -0.1240654 -0.599192010 FALSE
## 44 -1 -0.1240654 0.195183656 TRUE
## 45 -1 -0.1240654 1.121455659 TRUE
## 46 -1 -0.1240654 -0.408841615 FALSE
## 47 3 -0.1240654 0.536915074 FALSE
## 48 -1 -0.1240654 0.931105263 TRUE
## 49 -1 -0.1240654 2.950018512 TRUE
## 50 -1 -0.1240654 -0.997878665 FALSE
## 51 -1 -0.1240654 0.118743733 TRUE
## 52 2 -0.1240654 0.178696613 FALSE
## 53 -1 -0.1240654 -0.534742664 FALSE
## 54 -1 -0.1240654 0.899630001 TRUE
## 55 -1 -0.1240654 1.187403827 TRUE
## 56 -1 -0.1240654 1.359768358 TRUE
## 57 -1 -0.1240654 -1.513473437 FALSE
## 58 -1 -0.1240654 -1.526962835 FALSE
## 59 -1 -0.1240654 -1.526962835 FALSE
## 60 -1 -0.1240654 1.830398470 TRUE
## 61 -1 -0.1240654 0.431997533 TRUE
## 62 -1 -0.1240654 -0.966403403 FALSE
## 63 -1 -0.1240654 0.724267826 TRUE
## 64 -1 -0.1240654 0.605860887 TRUE
## 65 -1 -0.1240654 2.845100972 TRUE
## 66 -1 -0.1240654 0.791714816 TRUE
## 67 -1 -0.1240654 0.195183656 TRUE
## 68 -1 -0.1240654 -0.585702612 FALSE
## 69 -1 -0.1240654 -0.952914005 FALSE
## 70 -1 -0.1240654 -0.345891090 FALSE
## 71 -1 -0.1240654 1.392742443 TRUE
## 72 -1 -0.1240654 -1.090805630 FALSE
## 73 -1 -0.1240654 -0.669636645 FALSE
## 74 -1 -0.1240654 0.854665341 TRUE
## 75 -1 -0.1240654 -0.818020024 FALSE
## 76 -1 -0.1240654 0.707780783 TRUE
## 77 -1 -0.1240654 0.794712460 TRUE
## 78 -1 -0.1240654 -0.993382199 FALSE
## 79 1 -0.1240654 1.981779493 TRUE
## 80 -1 -0.1240654 1.139441523 TRUE
## 81 -1 -0.1240654 1.993770069 TRUE
## 82 -1 -0.1240654 -0.855490574 FALSE
## 83 -1 -0.1240654 0.773728952 TRUE
## 84 -1 -0.1240654 -0.209498287 TRUE
## 85 -1 -0.1240654 -1.389071210 FALSE
## 86 -1 -0.1240654 0.917615865 TRUE
## 87 1 -0.1240654 0.530919786 FALSE
## 88 -1 -0.1240654 1.661031583 TRUE
## 89 1 -0.1240654 1.400236553 TRUE
## 90 -1 -0.1240654 0.309094128 TRUE
## 91 -1 -0.1240654 -1.191226705 FALSE
## 92 -1 -0.1240654 -0.317413472 FALSE
## 93 -1 -0.1240654 1.942810121 TRUE
## 94 -1 -0.1240654 0.688296097 TRUE
## 95 1 -0.1240654 1.849883156 TRUE
## 96 -1 -0.1240654 1.042018092 TRUE
## 97 -1 -0.1240654 0.925109975 TRUE
## 98 -1 -0.1240654 2.226087481 TRUE
## 99 -1 -0.1240654 -1.435534693 FALSE
## 100 3 -0.1240654 1.975784205 TRUE
## 101 -1 -0.1240654 -0.104580747 TRUE
## 102 -1 -0.1240654 0.791714816 TRUE
## 103 -1 -0.1240654 0.695790207 TRUE
## 104 1 -0.1240654 1.541125822 TRUE
## 105 1 -0.1240654 1.530634068 TRUE
## 106 -1 -0.1240654 -0.028140824 TRUE
## 107 -1 -0.1240654 0.312091772 TRUE
## 108 -1 -0.1240654 0.692792563 TRUE
## 109 -1 -0.1240654 0.718272538 TRUE
## 110 -1 -0.1240654 -0.552728528 FALSE
## 111 -1 -0.1240654 -0.620175519 FALSE
## 112 -1 -0.1240654 2.686225838 TRUE
## 113 2 -0.1240654 2.845100972 TRUE
## 114 -1 -0.1240654 2.107680542 TRUE
## 115 2 -0.1240654 0.390030517 FALSE
## 116 -1 -0.1240654 0.760239554 TRUE
## 117 -1 -0.1240654 0.297103552 TRUE
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## 1989 -1 -0.1240654 -0.476288605 FALSE
## 1990 -1 -0.1240654 1.503655271 TRUE
## 1991 -1 -0.1240654 -0.777551830 FALSE
## 1992 -1 -0.1240654 1.722483285 TRUE
## 1993 -1 -0.1240654 -0.639660205 FALSE
## 1994 -1 -0.1240654 0.252138892 TRUE
## 1995 -1 -0.1240654 -0.521253266 FALSE
## 1996 -1 -0.1240654 0.420006957 TRUE
## 1997 -1 -0.1240654 0.524924498 TRUE
## 1998 -1 -0.1240654 -0.767060076 FALSE
## 1999 -1 -0.1240654 -1.264668983 FALSE
## 2000 -1 -0.1240654 -0.294931142 FALSE
## 2001 -1 -0.1240654 0.677804343 TRUE
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## 2003 -1 -0.1240654 -1.011368064 FALSE
## 2004 -1 -0.1240654 -0.158538339 TRUE
## 2005 -1 -0.1240654 -0.696615441 FALSE
## 2006 1 -0.1240654 2.674235262 TRUE
## 2007 1 -0.1240654 1.383749511 TRUE
## 2008 -1 -0.1240654 0.487453948 TRUE
## 2009 -1 -0.1240654 0.135230775 TRUE
## 2010 -1 -0.1240654 -0.273947634 FALSE
## 2011 -1 -0.1240654 -1.257174873 FALSE
## 2012 -1 -0.1240654 0.967076991 TRUE
## 2013 -1 -0.1240654 -0.979892801 FALSE
## 2014 3 -0.1240654 0.433496355 FALSE
## 2015 3 -0.1240654 1.613069278 TRUE
## 2016 2 -0.1240654 0.105254335 FALSE
## 2017 -1 -0.1240654 -1.194224349 FALSE
## 2018 -1 -0.1240654 0.451482220 TRUE
## 2019 -1 -0.1240654 -0.656147247 FALSE
## 2020 2 -0.1240654 2.034238263 TRUE
## 2021 -1 -0.1240654 -0.985888089 FALSE
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## 2027 -1 -0.1240654 -0.537740308 FALSE
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## 2032 -1 -0.1240654 -0.518255622 FALSE
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## 2040 1 -0.1240654 0.749747800 FALSE
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## 2048 3 -0.1240654 0.159211927 FALSE
## 2049 -1 -0.1240654 -0.889963481 FALSE
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## 2051 1 -0.1240654 0.369047009 FALSE
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## 2053 -1 -0.1240654 -0.865982328 FALSE
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## 2055 -1 -0.1240654 -0.059616086 TRUE
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## 2057 -1 -0.1240654 -0.449309809 FALSE
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## 2059 2 -0.1240654 -0.837504710 FALSE
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## 2061 -1 -0.1240654 -1.537454589 FALSE
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## 2065 -1 -0.1240654 -0.710104839 FALSE
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## 2072 -1 -0.1240654 -1.069822122 FALSE
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## 2082 1 -0.1240654 0.463472796 FALSE
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## 2086 -1 -0.1240654 -1.709819121 FALSE
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## 2088 -1 -0.1240654 0.914618221 TRUE
## 2089 -1 -0.1240654 -0.819518846 FALSE
## 2090 -1 -0.1240654 -0.501768580 FALSE
## 2091 3 -0.1240654 2.167633422 TRUE
## 2092 -1 -0.1240654 1.788431454 TRUE
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## 2097 -1 -0.1240654 1.406231841 TRUE
## 2098 -1 -0.1240654 -0.633664917 FALSE
## 2099 1 -0.1240654 0.342068213 FALSE
## 2100 3 -0.1240654 0.577383268 FALSE
## 2101 -1 -0.1240654 -0.252964126 TRUE
## 2102 -1 -0.1240654 1.173914429 TRUE
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## 2104 -1 -0.1240654 -0.828511778 FALSE
## 2105 1 -0.1240654 1.283328436 TRUE
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## 2109 -1 -0.1240654 -1.476002887 FALSE
## 2110 -1 -0.1240654 0.082772005 TRUE
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## 2112 -1 -0.1240654 0.637336149 TRUE
## 2113 -1 -0.1240654 1.221876733 TRUE
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## 2122 -1 -0.1240654 -0.353385201 FALSE
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## 2127 -1 -0.1240654 -0.819518846 FALSE
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## 3013 3 -0.1240654 0.162209571 FALSE
## 3014 -1 -0.1240654 -0.701111907 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "mood_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "mood_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $psychosis_4factorv2
## $fit
## Hydra_k3 psychosis_4factorv2 visregFit visregLwr visregUpr
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## 2 1 -0.1829434 -0.01326611 -0.129061636 0.10252942
## 3 2 -0.1829434 -0.14002546 -0.262057661 -0.01799327
## 4 3 -0.1829434 0.12104976 0.002630312 0.23946920
##
## $res
## Hydra_k3 psychosis_4factorv2 visregRes visregPos
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## 1989 -1 -0.1829434 -0.085631147 FALSE
## 1990 -1 -0.1829434 1.163540069 TRUE
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## 1997 -1 -0.1829434 -1.841223126 FALSE
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## 1999 -1 -0.1829434 2.287595566 TRUE
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## 2014 3 -0.1829434 0.575694791 TRUE
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## 2019 -1 -0.1829434 -1.583047835 FALSE
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## 3014 -1 -0.1829434 -0.204788974 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "psychosis_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "psychosis_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $externalizing_4factorv2
## $fit
## Hydra_k3 externalizing_4factorv2 visregFit visregLwr visregUpr
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## 2 1 -0.4826753 0.09838441 -0.008179497 0.2049483
## 3 2 -0.4826753 0.11381343 0.001510057 0.2261168
## 4 3 -0.4826753 0.10639124 -0.002587404 0.2153699
##
## $res
## Hydra_k3 externalizing_4factorv2 visregRes visregPos
## 1 -1 -0.4826753 -1.202891317 FALSE
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## 3 -1 -0.4826753 -0.346785537 TRUE
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## 1996 -1 -0.4826753 -0.910728233 FALSE
## 1997 -1 -0.4826753 1.126108974 TRUE
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## 2014 3 -0.4826753 -0.177678223 FALSE
## 2015 3 -0.4826753 -0.525707116 FALSE
## 2016 2 -0.4826753 -0.726522051 FALSE
## 2017 -1 -0.4826753 -1.183262789 FALSE
## 2018 -1 -0.4826753 -0.764269220 FALSE
## 2019 -1 -0.4826753 -0.188247430 TRUE
## 2020 2 -0.4826753 1.060428901 TRUE
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## 3007 -1 -0.4826753 0.290386665 TRUE
## 3008 -1 -0.4826753 -0.984712683 FALSE
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## 3010 -1 -0.4826753 1.730063668 TRUE
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## 3013 3 -0.4826753 1.764791063 TRUE
## 3014 -1 -0.4826753 -0.684245223 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "externalizing_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "externalizing_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $phobias_4factorv2
## $fit
## Hydra_k3 phobias_4factorv2 visregFit visregLwr visregUpr
## 1 -1 -0.4007962 -0.31591040 -0.35347231 -0.27834850
## 2 1 -0.4007962 0.18553444 0.07190489 0.29916398
## 3 2 -0.4007962 -0.03522991 -0.15497947 0.08451964
## 4 3 -0.4007962 0.27202348 0.15581910 0.38822786
##
## $res
## Hydra_k3 phobias_4factorv2 visregRes visregPos
## 1 -1 -0.4007962 -0.331180665 FALSE
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## 2020 2 -0.4007962 1.402246266 TRUE
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## 2824 -1 -0.4007962 -0.372949989 FALSE
## 2825 -1 -0.4007962 -1.521606389 FALSE
## 2826 3 -0.4007962 0.366715117 TRUE
## 2827 1 -0.4007962 1.906958926 TRUE
## 2828 -1 -0.4007962 -1.773962719 FALSE
## 2829 3 -0.4007962 1.315226841 TRUE
## 2830 -1 -0.4007962 0.055185578 TRUE
## 2831 -1 -0.4007962 -0.183247644 TRUE
## 2832 -1 -0.4007962 -0.764537398 FALSE
## 2833 -1 -0.4007962 -0.938576247 FALSE
## 2834 2 -0.4007962 -1.018634117 FALSE
## 2835 -1 -0.4007962 -0.566133111 FALSE
## 2836 -1 -0.4007962 -0.341622996 FALSE
## 2837 -1 -0.4007962 -0.903768477 FALSE
## 2838 -1 -0.4007962 -0.003987630 TRUE
## 2839 -1 -0.4007962 -0.590498550 FALSE
## 2840 -1 -0.4007962 -1.056922664 FALSE
## 2841 -1 -0.4007962 -0.964682074 FALSE
## 2842 -1 -0.4007962 -1.281432778 FALSE
## 2843 -1 -0.4007962 1.722477747 TRUE
## 2844 -1 -0.4007962 -0.816749053 FALSE
## 2845 -1 -0.4007962 0.890572051 TRUE
## 2846 -1 -0.4007962 0.370195894 TRUE
## 2847 -1 -0.4007962 -0.884624204 FALSE
## 2848 3 -0.4007962 0.666061936 TRUE
## 2849 -1 -0.4007962 -0.665335254 FALSE
## 2850 3 -0.4007962 -1.612106590 FALSE
## 2851 -1 -0.4007962 -0.853297211 FALSE
## 2852 -1 -0.4007962 -0.839374103 FALSE
## 2853 -1 -0.4007962 -0.884624204 FALSE
## 2854 3 -0.4007962 0.552936685 TRUE
## 2855 -1 -0.4007962 -0.837633714 FALSE
## 2856 -1 -0.4007962 -0.900287700 FALSE
## 2857 3 -0.4007962 1.235168971 TRUE
## 2858 2 -0.4007962 -0.325959500 FALSE
## 2859 -1 -0.4007962 -0.178026479 TRUE
## 2860 -1 -0.4007962 0.074329852 TRUE
## 2861 -1 -0.4007962 -1.264028893 FALSE
## 2862 -1 -0.4007962 -1.103913153 FALSE
## 2863 -1 -0.4007962 -0.997749455 FALSE
## 2864 2 -0.4007962 1.595429387 TRUE
## 2865 -1 -0.4007962 -0.729729628 FALSE
## 2866 -1 -0.4007962 -1.070845771 FALSE
## 2867 -1 -0.4007962 -1.314500159 FALSE
## 2868 2 -0.4007962 1.135966827 TRUE
## 2869 3 -0.4007962 -1.819212820 FALSE
## 2870 -1 -0.4007962 -0.402536593 FALSE
## 2871 -1 -0.4007962 0.720013979 TRUE
## 2872 -1 -0.4007962 -0.277228622 TRUE
## 2873 -1 -0.4007962 -0.889845369 FALSE
## 2874 -1 -0.4007962 0.474619203 TRUE
## 2875 -1 -0.4007962 -1.185711411 FALSE
## 2876 -1 -0.4007962 0.535532800 TRUE
## 2877 -1 -0.4007962 -0.766277787 FALSE
## 2878 -1 -0.4007962 1.308265287 TRUE
## 2879 -1 -0.4007962 -0.907249254 FALSE
## 2880 -1 -0.4007962 0.786148742 TRUE
## 2881 -1 -0.4007962 -0.802825945 FALSE
## 2882 -1 -0.4007962 -0.453007859 FALSE
## 2883 -1 -0.4007962 0.183974326 TRUE
## 2884 -1 -0.4007962 -1.960184287 FALSE
## 2885 -1 -0.4007962 -1.267509670 FALSE
## 2886 -1 -0.4007962 -1.199634519 FALSE
## 2887 -1 -0.4007962 -0.056199285 TRUE
## 2888 -1 -0.4007962 -0.470411744 FALSE
## 2889 -1 -0.4007962 -1.130018980 FALSE
## 2890 -1 -0.4007962 -0.305074838 TRUE
## 2891 3 -0.4007962 -0.003987630 FALSE
## 2892 -1 -0.4007962 -1.173528692 FALSE
## 2893 1 -0.4007962 -1.481577454 FALSE
## 2894 -1 -0.4007962 -0.762797010 FALSE
## 2895 -1 -0.4007962 -0.936835858 FALSE
## 2896 -1 -0.4007962 0.817475735 TRUE
## 2897 -1 -0.4007962 -1.123057426 FALSE
## 2898 -1 -0.4007962 0.387599779 TRUE
## 2899 -1 -0.4007962 -0.433863586 FALSE
## 2900 1 -0.4007962 0.598186786 TRUE
## 2901 2 -0.4007962 0.932341375 TRUE
## 2902 -1 -0.4007962 -0.590498550 FALSE
## 2903 -1 -0.4007962 -0.701883413 FALSE
## 2904 -1 -0.4007962 -0.834152937 FALSE
## 2905 -1 -0.4007962 -0.273747845 TRUE
## 2906 1 -0.4007962 -1.081288102 FALSE
## 2907 -1 -0.4007962 -0.802825945 FALSE
## 2908 -1 -0.4007962 0.067368298 TRUE
## 2909 -1 -0.4007962 0.970629921 TRUE
## 2910 -1 -0.4007962 0.349311232 TRUE
## 2911 -1 -0.4007962 -0.959460908 FALSE
## 2912 3 -0.4007962 1.035024295 TRUE
## 2913 -1 -0.4007962 0.746119807 TRUE
## 2914 -1 -0.4007962 0.352792009 TRUE
## 2915 -1 -0.4007962 -0.082305112 TRUE
## 2916 -1 -0.4007962 -1.157865196 FALSE
## 2917 1 -0.4007962 0.960187590 TRUE
## 2918 1 -0.4007962 0.568600181 TRUE
## 2919 -1 -0.4007962 1.125524496 TRUE
## 2920 -1 -0.4007962 1.000216526 TRUE
## 2921 -1 -0.4007962 -0.461709802 FALSE
## 2922 -1 -0.4007962 0.599927174 TRUE
## 2923 -1 -0.4007962 -1.399779195 FALSE
## 2924 -1 -0.4007962 -0.865479930 FALSE
## 2925 -1 -0.4007962 1.029803130 TRUE
## 2926 -1 -0.4007962 -1.370192591 FALSE
## 2927 -1 -0.4007962 -1.081288102 FALSE
## 2928 2 -0.4007962 0.267512973 TRUE
## 2929 -1 -0.4007962 -0.600940881 FALSE
## 2930 -1 -0.4007962 -0.037055011 TRUE
## 2931 -1 -0.4007962 -0.144959097 TRUE
## 2932 1 -0.4007962 -1.746116504 FALSE
## 2933 3 -0.4007962 -0.989047513 FALSE
## 2934 -1 -0.4007962 -0.696662247 FALSE
## 2935 -1 -0.4007962 -0.562652334 FALSE
## 2936 -1 -0.4007962 -0.872441484 FALSE
## 2937 -1 -0.4007962 -0.127555213 TRUE
## 2938 -1 -0.4007962 -0.406017370 FALSE
## 2939 -1 -0.4007962 0.988033806 TRUE
## 2940 3 -0.4007962 0.880129720 TRUE
## 2941 -1 -0.4007962 -1.436327353 FALSE
## 2942 -1 -0.4007962 0.765264080 TRUE
## 2943 1 -0.4007962 0.834879619 TRUE
## 2944 -1 -0.4007962 -1.389336864 FALSE
## 2945 -1 -0.4007962 -0.660114089 FALSE
## 2946 -1 -0.4007962 -1.036038002 FALSE
## 2947 -1 -0.4007962 -1.408481138 FALSE
## 2948 -1 -0.4007962 -0.331180665 FALSE
## 2949 -1 -0.4007962 -0.846335657 FALSE
## 2950 3 -0.4007962 0.330166959 TRUE
## 2951 3 -0.4007962 0.486801923 TRUE
## 2952 -1 -0.4007962 -1.434586965 FALSE
## 2953 -1 -0.4007962 -0.303334450 TRUE
## 2954 -1 -0.4007962 0.648658052 TRUE
## 2955 3 -0.4007962 -0.221536191 FALSE
## 2956 -1 -0.4007962 -0.602681269 FALSE
## 2957 -1 -0.4007962 0.358013175 TRUE
## 2958 -1 -0.4007962 -0.903768477 FALSE
## 2959 -1 -0.4007962 -1.565116101 FALSE
## 2960 -1 -0.4007962 -0.999489844 FALSE
## 2961 3 -0.4007962 1.727698912 TRUE
## 2962 -1 -0.4007962 0.791369907 TRUE
## 2963 -1 -0.4007962 -0.886364592 FALSE
## 2964 1 -0.4007962 1.586727445 TRUE
## 2965 -1 -0.4007962 1.015880022 TRUE
## 2966 -1 -0.4007962 0.683465821 TRUE
## 2967 -1 -0.4007962 1.363957719 TRUE
## 2968 -1 -0.4007962 0.754821749 TRUE
## 2969 -1 -0.4007962 1.054168569 TRUE
## 2970 -1 -0.4007962 -0.867220319 FALSE
## 2971 1 -0.4007962 0.027339363 FALSE
## 2972 -1 -0.4007962 -0.613123600 FALSE
## 2973 1 -0.4007962 -0.037055011 FALSE
## 2974 -1 -0.4007962 -1.152644030 FALSE
## 2975 1 -0.4007962 2.013122624 TRUE
## 2976 -1 -0.4007962 -0.047497342 TRUE
## 2977 3 -0.4007962 4.340022028 TRUE
## 2978 2 -0.4007962 1.282159460 TRUE
## 2979 -1 -0.4007962 -0.113632105 TRUE
## 2980 3 -0.4007962 1.132486050 TRUE
## 2981 1 -0.4007962 1.196880424 TRUE
## 2982 2 -0.4007962 0.464176872 TRUE
## 2983 3 -0.4007962 2.937268909 TRUE
## 2984 -1 -0.4007962 1.598910164 TRUE
## 2985 -1 -0.4007962 1.701593085 TRUE
## 2986 2 -0.4007962 1.078534007 TRUE
## 2987 1 -0.4007962 0.981072252 TRUE
## 2988 3 -0.4007962 1.880853099 TRUE
## 2989 -1 -0.4007962 -1.027336059 FALSE
## 2990 1 -0.4007962 1.602390941 TRUE
## 2991 -1 -0.4007962 -2.052424877 FALSE
## 2992 -1 -0.4007962 -0.597460104 FALSE
## 2993 -1 -0.4007962 0.338868901 TRUE
## 2994 -1 -0.4007962 -0.964682074 FALSE
## 2995 -1 -0.4007962 -0.388613485 FALSE
## 2996 -1 -0.4007962 1.374400050 TRUE
## 2997 -1 -0.4007962 1.028062741 TRUE
## 2998 -1 -0.4007962 -0.780200894 FALSE
## 2999 -1 -0.4007962 -0.592238938 FALSE
## 3000 -1 -0.4007962 -0.924653139 FALSE
## 3001 -1 -0.4007962 0.445032599 TRUE
## 3002 -1 -0.4007962 1.623275603 TRUE
## 3003 -1 -0.4007962 -1.211817239 FALSE
## 3004 -1 -0.4007962 -1.429365799 FALSE
## 3005 1 -0.4007962 -1.845318647 FALSE
## 3006 -1 -0.4007962 -0.144959097 TRUE
## 3007 -1 -0.4007962 -0.473892521 FALSE
## 3008 -1 -0.4007962 -0.353805716 FALSE
## 3009 -1 -0.4007962 0.961927979 TRUE
## 3010 -1 -0.4007962 -0.670556420 FALSE
## 3011 -1 -0.4007962 -0.113632105 TRUE
## 3012 -1 -0.4007962 -0.198911141 TRUE
## 3013 3 -0.4007962 1.353515388 TRUE
## 3014 -1 -0.4007962 -0.842854880 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "phobias_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "phobias_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"
##
## $overall_psychopathology_4factorv2
## $fit
## Hydra_k3 overall_psychopathology_4factorv2 visregFit visregLwr
## 1 -1 -0.4027408 -0.6695342 -0.7032364
## 2 1 -0.4027408 0.9080526 0.8060991
## 3 2 -0.4027408 0.8915314 0.7840867
## 4 3 -0.4027408 1.0121420 0.9078781
## visregUpr
## 1 -0.6358319
## 2 1.0100062
## 3 0.9989761
## 4 1.1164058
##
## $res
## Hydra_k3 overall_psychopathology_4factorv2 visregRes visregPos
## 1 -1 -0.4027408 0.337138191 TRUE
## 2 -1 -0.4027408 0.600484966 TRUE
## 3 -1 -0.4027408 1.450718839 TRUE
## 4 -1 -0.4027408 -1.259244878 FALSE
## 5 -1 -0.4027408 -0.563256973 TRUE
## 6 -1 -0.4027408 -0.574543263 TRUE
## 7 -1 -0.4027408 0.001057545 TRUE
## 8 -1 -0.4027408 2.017541421 TRUE
## 9 3 -0.4027408 0.816178515 FALSE
## 10 -1 -0.4027408 0.628073676 TRUE
## 11 2 -0.4027408 0.882642225 FALSE
## 12 -1 -0.4027408 -0.252256972 TRUE
## 13 3 -0.4027408 1.571105936 TRUE
## 14 -1 -0.4027408 1.105859967 TRUE
## 15 -1 -0.4027408 -0.452902134 TRUE
## 16 -1 -0.4027408 0.792351902 TRUE
## 17 -1 -0.4027408 0.221767223 TRUE
## 18 -1 -0.4027408 -0.177015036 TRUE
## 19 -1 -0.4027408 0.737174482 TRUE
## 20 -1 -0.4027408 -0.042833584 TRUE
## 21 -1 -0.4027408 -0.098011004 TRUE
## 22 -1 -0.4027408 1.147243032 TRUE
## 23 -1 -0.4027408 -0.168236810 TRUE
## 24 -1 -0.4027408 -0.140648101 TRUE
## 25 3 -0.4027408 1.016823677 TRUE
## 26 -1 -0.4027408 1.315283355 TRUE
## 27 -1 -0.4027408 0.743444644 TRUE
## 28 -1 -0.4027408 -0.135631972 TRUE
## 29 -1 -0.4027408 -0.913131974 FALSE
## 30 -1 -0.4027408 -1.118793265 FALSE
## 31 -1 -0.4027408 0.898944644 TRUE
## 32 -1 -0.4027408 -1.225386007 FALSE
## 33 -1 -0.4027408 -0.357595682 TRUE
## 34 -1 -0.4027408 0.199194642 TRUE
## 35 -1 -0.4027408 -0.264797295 TRUE
## 36 -1 -0.4027408 0.086331739 TRUE
## 37 -1 -0.4027408 -0.001450520 TRUE
## 38 2 -0.4027408 -0.291131972 FALSE
## 39 -1 -0.4027408 -0.577051328 TRUE
## 40 -1 -0.4027408 -0.799015038 FALSE
## 41 -1 -0.4027408 -0.301164230 TRUE
## 42 1 -0.4027408 0.931549483 TRUE
## 43 -1 -0.4027408 -0.708724715 FALSE
## 44 -1 -0.4027408 -0.501809392 TRUE
## 45 -1 -0.4027408 -0.175761004 TRUE
## 46 -1 -0.4027408 0.392315610 TRUE
## 47 3 -0.4027408 0.778557547 FALSE
## 48 -1 -0.4027408 -1.196543265 FALSE
## 49 -1 -0.4027408 0.121444642 TRUE
## 50 -1 -0.4027408 -0.873002941 FALSE
## 51 -1 -0.4027408 1.019331741 TRUE
## 52 2 -0.4027408 1.522198678 TRUE
## 53 -1 -0.4027408 0.022376093 TRUE
## 54 -1 -0.4027408 0.254372062 TRUE
## 55 -1 -0.4027408 -0.301164230 TRUE
## 56 -1 -0.4027408 -0.662325522 TRUE
## 57 -1 -0.4027408 -0.267305359 TRUE
## 58 -1 -0.4027408 -0.158204552 TRUE
## 59 -1 -0.4027408 0.175368029 TRUE
## 60 -1 -0.4027408 -0.338785198 TRUE
## 61 -1 -0.4027408 -0.851684393 FALSE
## 62 -1 -0.4027408 0.500162385 TRUE
## 63 -1 -0.4027408 -0.046595681 TRUE
## 64 -1 -0.4027408 -0.003958584 TRUE
## 65 -1 -0.4027408 0.093855932 TRUE
## 66 -1 -0.4027408 -0.548208586 TRUE
## 67 -1 -0.4027408 -0.521873908 TRUE
## 68 -1 -0.4027408 -1.948962622 FALSE
## 69 -1 -0.4027408 -0.382676327 TRUE
## 70 -1 -0.4027408 -0.728789231 FALSE
## 71 -1 -0.4027408 -0.567019070 TRUE
## 72 -1 -0.4027408 0.304533352 TRUE
## 73 -1 -0.4027408 -0.666087618 TRUE
## 74 -1 -0.4027408 -1.017216652 FALSE
## 75 -1 -0.4027408 -1.698156170 FALSE
## 76 -1 -0.4027408 -0.521873908 TRUE
## 77 -1 -0.4027408 0.328359965 TRUE
## 78 -1 -0.4027408 -0.128107778 TRUE
## 79 1 -0.4027408 -0.498047295 FALSE
## 80 -1 -0.4027408 2.149214809 TRUE
## 81 -1 -0.4027408 0.463795449 TRUE
## 82 -1 -0.4027408 1.678952711 TRUE
## 83 -1 -0.4027408 -0.041579552 TRUE
## 84 -1 -0.4027408 -1.339502943 FALSE
## 85 -1 -0.4027408 -0.348817456 TRUE
## 86 -1 -0.4027408 0.189162384 TRUE
## 87 1 -0.4027408 2.160501099 TRUE
## 88 -1 -0.4027408 -0.346309392 TRUE
## 89 1 -0.4027408 -0.032801326 FALSE
## 90 -1 -0.4027408 0.199194642 TRUE
## 91 -1 -0.4027408 0.954122064 TRUE
## 92 -1 -0.4027408 1.148497064 TRUE
## 93 -1 -0.4027408 0.096363997 TRUE
## 94 -1 -0.4027408 0.581674482 TRUE
## 95 1 -0.4027408 1.611234969 TRUE
## 96 -1 -0.4027408 1.028109967 TRUE
## 97 -1 -0.4027408 0.569134159 TRUE
## 98 -1 -0.4027408 0.443730933 TRUE
## 99 -1 -0.4027408 -0.062898100 TRUE
## 100 3 -0.4027408 0.290738997 FALSE
## 101 -1 -0.4027408 -1.032265039 FALSE
## 102 -1 -0.4027408 -0.103027133 TRUE
## 103 -1 -0.4027408 0.049964803 TRUE
## 104 1 -0.4027408 -0.410265037 FALSE
## 105 1 -0.4027408 0.276944642 FALSE
## 106 -1 -0.4027408 0.149033352 TRUE
## 107 -1 -0.4027408 -1.102490845 FALSE
## 108 -1 -0.4027408 -0.059136004 TRUE
## 109 -1 -0.4027408 0.915247064 TRUE
## 110 -1 -0.4027408 -0.916894071 FALSE
## 111 -1 -0.4027408 -0.931942458 FALSE
## 112 -1 -0.4027408 -0.519365844 TRUE
## 113 2 -0.4027408 0.039932545 FALSE
## 114 -1 -0.4027408 -0.644769070 TRUE
## 115 2 -0.4027408 1.297726903 TRUE
## 116 -1 -0.4027408 0.319581739 TRUE
## 117 -1 -0.4027408 1.061968838 TRUE
## 118 -1 -0.4027408 -0.583321489 TRUE
## 119 -1 -0.4027408 0.250609965 TRUE
## 120 -1 -0.4027408 -1.071140039 FALSE
## 121 -1 -0.4027408 0.308295449 TRUE
## 122 -1 -0.4027408 -0.587083586 TRUE
## 123 -1 -0.4027408 0.091347868 TRUE
## 124 -1 -0.4027408 0.165335771 TRUE
## 125 3 -0.4027408 0.804892225 FALSE
## 126 -1 -0.4027408 -0.905607780 FALSE
## 127 -1 -0.4027408 -0.732551328 FALSE
## 128 -1 -0.4027408 1.312775290 TRUE
## 129 2 -0.4027408 1.330331742 TRUE
## 130 1 -0.4027408 1.275154323 TRUE
## 131 -1 -0.4027408 0.250609965 TRUE
## 132 -1 -0.4027408 -0.657309392 TRUE
## 133 -1 -0.4027408 -1.296865846 FALSE
## 134 -1 -0.4027408 -0.587083586 TRUE
## 135 1 -0.4027408 0.046202706 FALSE
## 136 1 -0.4027408 0.228037384 FALSE
## 137 1 -0.4027408 2.026319647 TRUE
## 138 -1 -0.4027408 0.645630127 TRUE
## 139 -1 -0.4027408 -1.377123911 FALSE
## 140 -1 -0.4027408 -0.429075521 TRUE
## 141 -1 -0.4027408 -0.797761006 FALSE
## 142 -1 -0.4027408 0.791097870 TRUE
## 143 -1 -0.4027408 -0.121837617 TRUE
## 144 -1 -0.4027408 -0.202095682 TRUE
## 145 3 -0.4027408 0.146525287 FALSE
## 146 -1 -0.4027408 -0.416535198 TRUE
## 147 -1 -0.4027408 -0.205857778 TRUE
## 148 -1 -0.4027408 -1.877482783 FALSE
## 149 -1 -0.4027408 0.486368030 TRUE
## 150 -1 -0.4027408 0.392315610 TRUE
## 151 -1 -0.4027408 0.189162384 TRUE
## 152 -1 -0.4027408 0.957884160 TRUE
## 153 1 -0.4027408 0.807400289 FALSE
## 154 -1 -0.4027408 0.288230933 TRUE
## 155 -1 -0.4027408 0.946597870 TRUE
## 156 -1 -0.4027408 -0.796506974 FALSE
## 157 -1 -0.4027408 -1.417252943 FALSE
## 158 -1 -0.4027408 -0.114313423 TRUE
## 159 1 -0.4027408 -0.331261004 FALSE
## 160 -1 -0.4027408 -1.152652136 FALSE
## 161 -1 -0.4027408 0.557847869 TRUE
## 162 -1 -0.4027408 -0.244732778 TRUE
## 163 -1 -0.4027408 -1.260498910 FALSE
## 164 2 -0.4027408 0.718363998 FALSE
## 165 -1 -0.4027408 -0.920656167 FALSE
## 166 -1 -0.4027408 -0.424059392 TRUE
## 167 -1 -0.4027408 -0.085470681 TRUE
## 168 -1 -0.4027408 1.429400291 TRUE
## 169 -1 -0.4027408 -1.464906169 FALSE
## 170 2 -0.4027408 -0.358849714 FALSE
## 171 -1 -0.4027408 -1.783430363 FALSE
## 172 -1 -0.4027408 -0.161966649 TRUE
## 173 -1 -0.4027408 0.902706741 TRUE
## 174 -1 -0.4027408 0.708331740 TRUE
## 175 -1 -0.4027408 0.304533352 TRUE
## 176 -1 -0.4027408 0.856307547 TRUE
## 177 1 -0.4027408 0.744698676 FALSE
## 178 1 -0.4027408 2.225710777 TRUE
## 179 -1 -0.4027408 -0.232192456 TRUE
## 180 -1 -0.4027408 -1.679345686 FALSE
## 181 -1 -0.4027408 -1.767127944 FALSE
## 182 2 -0.4027408 0.688267224 FALSE
## 183 -1 -0.4027408 -0.963293264 FALSE
## 184 -1 -0.4027408 -0.351325521 TRUE
## 185 -1 -0.4027408 0.732158353 TRUE
## 186 -1 -0.4027408 1.319045452 TRUE
## 187 -1 -0.4027408 0.214243029 TRUE
## 188 1 -0.4027408 0.551577708 FALSE
## 189 1 -0.4027408 -0.089232778 FALSE
## 190 -1 -0.4027408 -0.378914230 TRUE
## 191 -1 -0.4027408 -0.372644069 TRUE
## 192 3 -0.4027408 1.864549486 TRUE
## 193 -1 -0.4027408 0.597976901 TRUE
## 194 -1 -0.4027408 -0.663579554 TRUE
## 195 -1 -0.4027408 -0.449140037 TRUE
## 196 2 -0.4027408 2.007509163 TRUE
## 197 -1 -0.4027408 0.998013193 TRUE
## 198 -1 -0.4027408 1.479561581 TRUE
## 199 2 -0.4027408 0.581674482 FALSE
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## 1839 3 -0.4027408 1.549787388 TRUE
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## 1844 -1 -0.4027408 -0.469204553 TRUE
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## 1848 -1 -0.4027408 -0.625958586 TRUE
## 1849 -1 -0.4027408 -1.774652138 FALSE
## 1850 3 -0.4027408 0.692029321 FALSE
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## 1852 -1 -0.4027408 -1.690631976 FALSE
## 1853 -1 -0.4027408 -1.456127943 FALSE
## 1854 -1 -0.4027408 0.500162385 TRUE
## 1855 3 -0.4027408 0.885150289 FALSE
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## 1857 -1 -0.4027408 0.466303514 TRUE
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## 1870 -1 -0.4027408 -0.319974714 TRUE
## 1871 -1 -0.4027408 1.114638193 TRUE
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## 1873 2 -0.4027408 1.069493032 TRUE
## 1874 -1 -0.4027408 0.364726901 TRUE
## 1875 -1 -0.4027408 1.376730936 TRUE
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## 1883 -1 -0.4027408 -0.861716651 FALSE
## 1884 -1 -0.4027408 -0.223414230 TRUE
## 1885 3 -0.4027408 1.602456743 TRUE
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## 1889 1 -0.4027408 1.103351903 TRUE
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## 1898 -1 -0.4027408 -0.943228748 FALSE
## 1899 2 -0.4027408 1.386763194 TRUE
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## 1902 -1 -0.4027408 -0.965801329 FALSE
## 1903 -1 -0.4027408 -1.857418267 FALSE
## 1904 3 -0.4027408 1.112130129 TRUE
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## 1906 -1 -0.4027408 -0.839144070 FALSE
## 1907 -1 -0.4027408 0.595468837 TRUE
## 1908 -1 -0.4027408 -1.956486815 FALSE
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## 1911 2 -0.4027408 0.212988997 FALSE
## 1912 -1 -0.4027408 -0.409011005 TRUE
## 1913 3 -0.4027408 0.999267225 FALSE
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## 1918 -1 -0.4027408 -0.484252940 TRUE
## 1919 1 -0.4027408 2.136674486 TRUE
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## 1922 2 -0.4027408 -0.484252940 FALSE
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## 1925 1 -0.4027408 -0.299910198 FALSE
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## 1927 -1 -0.4027408 -1.952724719 FALSE
## 1928 -1 -0.4027408 1.672682550 TRUE
## 1929 -1 -0.4027408 0.070029319 TRUE
## 1930 -1 -0.4027408 -1.586547298 FALSE
## 1931 3 -0.4027408 0.544053514 FALSE
## 1932 -1 -0.4027408 1.626283356 TRUE
## 1933 -1 -0.4027408 -1.507543266 FALSE
## 1934 -1 -0.4027408 -0.242224714 TRUE
## 1935 2 -0.4027408 -0.016498907 FALSE
## 1936 -1 -0.4027408 -0.762648102 FALSE
## 1937 -1 -0.4027408 1.183609968 TRUE
## 1938 -1 -0.4027408 0.029900287 TRUE
## 1939 -1 -0.4027408 0.202956739 TRUE
## 1940 -1 -0.4027408 -0.321228746 TRUE
## 1941 -1 -0.4027408 -1.708188428 FALSE
## 1942 1 -0.4027408 0.310803513 FALSE
## 1943 -1 -0.4027408 -1.740793266 FALSE
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## 1947 -1 -0.4027408 -0.614672296 TRUE
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## 1951 -1 -0.4027408 -1.673075524 FALSE
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## 1953 -1 -0.4027408 -1.678091653 FALSE
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## 1957 -1 -0.4027408 -1.247958588 FALSE
## 1958 -1 -0.4027408 -0.964547297 FALSE
## 1959 -1 -0.4027408 -1.041043265 FALSE
## 1960 -1 -0.4027408 -1.548926330 FALSE
## 1961 -1 -0.4027408 -1.892531170 FALSE
## 1962 -1 -0.4027408 0.275690610 TRUE
## 1963 -1 -0.4027408 -0.179523101 TRUE
## 1964 -1 -0.4027408 -0.704962618 FALSE
## 1965 -1 -0.4027408 -1.670567460 FALSE
## 1966 -1 -0.4027408 -0.287369875 TRUE
## 1967 -1 -0.4027408 0.374759159 TRUE
## 1968 -1 -0.4027408 -0.941974716 FALSE
## 1969 3 -0.4027408 -0.069168262 FALSE
## 1970 2 -0.4027408 1.474545452 TRUE
## 1971 -1 -0.4027408 -1.911341654 FALSE
## 1972 -1 -0.4027408 -0.886797296 FALSE
## 1973 -1 -0.4027408 -1.501273105 FALSE
## 1974 -1 -0.4027408 -1.481208588 FALSE
## 1975 -1 -0.4027408 -1.915103751 FALSE
## 1976 -1 -0.4027408 -1.261752943 FALSE
## 1977 -1 -0.4027408 -1.852402138 FALSE
## 1978 -1 -0.4027408 -1.780922299 FALSE
## 1979 -1 -0.4027408 -0.896829554 FALSE
## 1980 -1 -0.4027408 -0.866732780 FALSE
## 1981 -1 -0.4027408 -1.916357783 FALSE
## 1982 -1 -0.4027408 -1.120047297 FALSE
## 1983 -1 -0.4027408 -1.354551330 FALSE
## 1984 -1 -0.4027408 -1.400950524 FALSE
## 1985 -1 -0.4027408 -1.345773104 FALSE
## 1986 -1 -0.4027408 -1.316930362 FALSE
## 1987 -1 -0.4027408 -1.363329556 FALSE
## 1988 -1 -0.4027408 -0.324990843 TRUE
## 1989 -1 -0.4027408 -1.311914233 FALSE
## 1990 -1 -0.4027408 -0.195825520 TRUE
## 1991 -1 -0.4027408 -1.898801331 FALSE
## 1992 -1 -0.4027408 -0.904353748 FALSE
## 1993 -1 -0.4027408 -0.962039232 FALSE
## 1994 -1 -0.4027408 -1.000914232 FALSE
## 1995 -1 -0.4027408 -0.785220683 FALSE
## 1996 -1 -0.4027408 -1.165192458 FALSE
## 1997 -1 -0.4027408 0.036170448 TRUE
## 1998 -1 -0.4027408 -1.634200524 FALSE
## 1999 -1 -0.4027408 -0.629720683 TRUE
## 2000 -1 -0.4027408 -0.763902135 FALSE
## 2001 -1 -0.4027408 -0.298656166 TRUE
## 2002 -1 -0.4027408 -0.643515038 TRUE
## 2003 -1 -0.4027408 -1.211591652 FALSE
## 2004 -1 -0.4027408 -0.292386004 TRUE
## 2005 -1 -0.4027408 -1.907579557 FALSE
## 2006 1 -0.4027408 -0.039071488 FALSE
## 2007 1 -0.4027408 1.411843839 TRUE
## 2008 -1 -0.4027408 -0.990881974 FALSE
## 2009 -1 -0.4027408 -1.249212620 FALSE
## 2010 -1 -0.4027408 0.243085771 TRUE
## 2011 -1 -0.4027408 -0.694930360 FALSE
## 2012 -1 -0.4027408 -1.096220684 FALSE
## 2013 -1 -0.4027408 -0.190809391 TRUE
## 2014 3 -0.4027408 1.004283354 FALSE
## 2015 3 -0.4027408 -0.436599714 FALSE
## 2016 2 -0.4027408 0.572896256 FALSE
## 2017 -1 -0.4027408 -0.778950522 FALSE
## 2018 -1 -0.4027408 -1.715712621 FALSE
## 2019 -1 -0.4027408 -0.168236810 TRUE
## 2020 2 -0.4027408 0.737174482 FALSE
## 2021 -1 -0.4027408 -1.073648103 FALSE
## 2022 -1 -0.4027408 -1.069886007 FALSE
## 2023 -1 -0.4027408 -0.697438425 FALSE
## 2024 -1 -0.4027408 1.381747065 TRUE
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## 2026 -1 -0.4027408 0.991743031 TRUE
## 2027 -1 -0.4027408 0.285722868 TRUE
## 2028 -1 -0.4027408 0.776049483 TRUE
## 2029 2 -0.4027408 1.404319646 TRUE
## 2030 -1 -0.4027408 -0.071676326 TRUE
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## 2032 -1 -0.4027408 -0.455410198 TRUE
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## 2034 -1 -0.4027408 -1.421015040 FALSE
## 2035 -1 -0.4027408 0.471319643 TRUE
## 2036 -1 -0.4027408 -1.150144071 FALSE
## 2037 -1 -0.4027408 -0.652293263 TRUE
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## 2039 -1 -0.4027408 -0.896829554 FALSE
## 2040 1 -0.4027408 1.041904322 TRUE
## 2041 -1 -0.4027408 0.490130127 TRUE
## 2042 1 -0.4027408 1.949823679 TRUE
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## 2044 -1 -0.4027408 0.077553513 TRUE
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## 2046 -1 -0.4027408 -1.028502942 FALSE
## 2047 -1 -0.4027408 -0.022769068 TRUE
## 2048 3 -0.4027408 1.329077710 TRUE
## 2049 -1 -0.4027408 0.179130126 TRUE
## 2050 1 -0.4027408 1.841976905 TRUE
## 2051 1 -0.4027408 0.744698676 FALSE
## 2052 -1 -0.4027408 -0.156950520 TRUE
## 2053 -1 -0.4027408 -1.844877944 FALSE
## 2054 -1 -0.4027408 -1.160176329 FALSE
## 2055 -1 -0.4027408 -0.615926328 TRUE
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## 2057 -1 -0.4027408 1.013061580 TRUE
## 2058 -1 -0.4027408 -0.637244876 TRUE
## 2059 2 -0.4027408 1.199912387 TRUE
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## 2075 -1 -0.4027408 -1.862434396 FALSE
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## 2088 -1 -0.4027408 -0.555732779 TRUE
## 2089 -1 -0.4027408 0.556593837 TRUE
## 2090 -1 -0.4027408 0.202956739 TRUE
## 2091 3 -0.4027408 1.028109967 TRUE
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## 2095 -1 -0.4027408 0.812416418 TRUE
## 2096 2 -0.4027408 0.813670450 FALSE
## 2097 -1 -0.4027408 -1.204067459 FALSE
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## 2099 1 -0.4027408 0.870101902 FALSE
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## 2102 -1 -0.4027408 0.483859966 TRUE
## 2103 -1 -0.4027408 0.655662385 TRUE
## 2104 -1 -0.4027408 -1.527607782 FALSE
## 2105 1 -0.4027408 -0.062898100 FALSE
## 2106 3 -0.4027408 1.803101905 TRUE
## 2107 -1 -0.4027408 0.814924483 TRUE
## 2108 3 -0.4027408 1.534739001 TRUE
## 2109 -1 -0.4027408 -0.131869875 TRUE
## 2110 -1 -0.4027408 1.149751096 TRUE
## 2111 2 -0.4027408 0.501416417 FALSE
## 2112 -1 -0.4027408 -0.751361812 FALSE
## 2113 -1 -0.4027408 -1.744555363 FALSE
## 2114 3 -0.4027408 0.353440610 FALSE
## 2115 -1 -0.4027408 -0.420297295 TRUE
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## 2118 1 -0.4027408 0.941581741 TRUE
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## 2120 -1 -0.4027408 1.534739001 TRUE
## 2121 -1 -0.4027408 1.063222870 TRUE
## 2122 -1 -0.4027408 -0.619688425 TRUE
## 2123 -1 -0.4027408 -1.083680361 FALSE
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## 2125 -1 -0.4027408 -1.700664234 FALSE
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## 2127 -1 -0.4027408 -1.653011008 FALSE
## 2128 -1 -0.4027408 -0.046595681 TRUE
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## 2130 3 -0.4027408 0.661932547 FALSE
## 2131 -1 -0.4027408 0.531513191 TRUE
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## 2133 -1 -0.4027408 -1.226640039 FALSE
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## 2135 1 -0.4027408 1.334093839 TRUE
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## 2140 -1 -0.4027408 0.917755128 TRUE
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## 2142 -1 -0.4027408 -0.642261005 TRUE
## 2143 -1 -0.4027408 -0.514349715 TRUE
## 2144 -1 -0.4027408 -1.943946493 FALSE
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## 2146 -1 -0.4027408 -0.153188423 TRUE
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## 2150 -1 -0.4027408 -1.943946493 FALSE
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## 2152 3 -0.4027408 1.561073678 TRUE
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## 2156 -1 -0.4027408 -1.740793266 FALSE
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## 2162 -1 -0.4027408 -0.768918264 FALSE
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## 2167 -1 -0.4027408 -0.704962618 FALSE
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## 2181 -1 -0.4027408 -0.091740843 TRUE
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## 2647 -1 -0.4027408 -1.336994878 FALSE
## 2648 -1 -0.4027408 -1.338248911 FALSE
## 2649 -1 -0.4027408 -1.184002942 FALSE
## 2650 -1 -0.4027408 -1.486224717 FALSE
## 2651 -1 -0.4027408 -1.161430362 FALSE
## 2652 -1 -0.4027408 -1.500019072 FALSE
## 2653 -1 -0.4027408 -2.066841654 FALSE
## 2654 -1 -0.4027408 -1.037281168 FALSE
## 2655 -1 -0.4027408 -1.370853749 FALSE
## 2656 -1 -0.4027408 -0.929434393 FALSE
## 2657 -1 -0.4027408 -1.163938426 FALSE
## 2658 -1 -0.4027408 -0.817825522 FALSE
## 2659 -1 -0.4027408 -0.545700521 TRUE
## 2660 -1 -0.4027408 -0.722519070 FALSE
## 2661 -1 -0.4027408 -1.862434396 FALSE
## 2662 -1 -0.4027408 -0.529398102 TRUE
## 2663 -1 -0.4027408 -0.647277134 TRUE
## 2664 -1 -0.4027408 -2.024204558 FALSE
## 2665 -1 -0.4027408 -1.940184396 FALSE
## 2666 -1 -0.4027408 -0.874256974 FALSE
## 2667 -1 -0.4027408 -1.339502943 FALSE
## 2668 -1 -0.4027408 -1.955232783 FALSE
## 2669 -1 -0.4027408 -1.222877942 FALSE
## 2670 -1 -0.4027408 -1.878736815 FALSE
## 2671 -1 -0.4027408 -1.370853749 FALSE
## 2672 -1 -0.4027408 -0.851684393 FALSE
## 2673 -1 -0.4027408 -1.385902136 FALSE
## 2674 -1 -0.4027408 -0.494285198 TRUE
## 2675 -1 -0.4027408 -1.961502945 FALSE
## 2676 -1 -0.4027408 -1.135095684 FALSE
## 2677 -1 -0.4027408 -1.550180363 FALSE
## 2678 -1 -0.4027408 0.327105933 TRUE
## 2679 -1 -0.4027408 -0.103027133 TRUE
## 2680 -1 -0.4027408 -0.294894069 TRUE
## 2681 -1 -0.4027408 -1.902563428 FALSE
## 2682 -1 -0.4027408 -1.236672297 FALSE
## 2683 -1 -0.4027408 -1.635454556 FALSE
## 2684 -1 -0.4027408 -1.190273104 FALSE
## 2685 -1 -0.4027408 -1.619152137 FALSE
## 2686 -1 -0.4027408 -0.687406167 FALSE
## 2687 -1 -0.4027408 -1.389664233 FALSE
## 2688 -1 -0.4027408 -1.585293266 FALSE
## 2689 -1 -0.4027408 -1.167700523 FALSE
## 2690 -1 -0.4027408 -1.236672297 FALSE
## 2691 -1 -0.4027408 -1.759603750 FALSE
## 2692 -1 -0.4027408 -1.064869877 FALSE
## 2693 3 -0.4027408 0.214243029 FALSE
## 2694 -1 -0.4027408 -1.405966653 FALSE
## 2695 -1 -0.4027408 -0.648531167 TRUE
## 2696 -1 -0.4027408 -1.883752944 FALSE
## 2697 -1 -0.4027408 -0.876765038 FALSE
## 2698 -1 -0.4027408 -0.409011005 TRUE
## 2699 -1 -0.4027408 -0.847922296 FALSE
## 2700 -1 -0.4027408 -0.812809393 FALSE
## 2701 -1 -0.4027408 -0.880527135 FALSE
## 2702 -1 -0.4027408 -1.953978751 FALSE
## 2703 -1 -0.4027408 -1.311914233 FALSE
## 2704 -1 -0.4027408 -1.408474717 FALSE
## 2705 -1 -0.4027408 -0.524381973 TRUE
## 2706 -1 -0.4027408 -1.814781170 FALSE
## 2707 -1 -0.4027408 -0.006466649 TRUE
## 2708 -1 -0.4027408 -0.538176328 TRUE
## 2709 -1 -0.4027408 1.444448678 TRUE
## 2710 -1 -0.4027408 0.246847868 TRUE
## 2711 -1 -0.4027408 -0.519365844 TRUE
## 2712 -1 -0.4027408 -1.496256975 FALSE
## 2713 1 -0.4027408 -0.436599714 FALSE
## 2714 -1 -0.4027408 -0.903099716 FALSE
## 2715 -1 -0.4027408 -0.173252940 TRUE
## 2716 -1 -0.4027408 1.257597871 TRUE
## 2717 -1 -0.4027408 -0.218398101 TRUE
## 2718 1 -0.4027408 -0.076692455 FALSE
## 2719 -1 -0.4027408 -1.665551331 FALSE
## 2720 -1 -0.4027408 0.631835772 TRUE
## 2721 -1 -0.4027408 0.858815612 TRUE
## 2722 -1 -0.4027408 0.293247062 TRUE
## 2723 -1 -0.4027408 -0.440361811 TRUE
## 2724 -1 -0.4027408 -0.577051328 TRUE
## 2725 2 -0.4027408 0.727142224 FALSE
## 2726 -1 -0.4027408 -1.497511008 FALSE
## 2727 3 -0.4027408 -0.269813424 FALSE
## 2728 -1 -0.4027408 -2.037998912 FALSE
## 2729 2 -0.4027408 1.119654322 TRUE
## 2730 -1 -0.4027408 -1.521337621 FALSE
## 2731 -1 -0.4027408 0.100126094 TRUE
## 2732 -1 -0.4027408 -0.330006972 TRUE
## 2733 -1 -0.4027408 -1.640470686 FALSE
## 2734 -1 -0.4027408 0.359710772 TRUE
## 2735 -1 -0.4027408 -1.933914235 FALSE
## 2736 -1 -0.4027408 -0.862970683 FALSE
## 2737 -1 -0.4027408 -1.518829556 FALSE
## 2738 -1 -0.4027408 -1.048567458 FALSE
## 2739 -1 -0.4027408 -0.796506974 FALSE
## 2740 -1 -0.4027408 -0.039071488 TRUE
## 2741 -1 -0.4027408 -0.113059391 TRUE
## 2742 -1 -0.4027408 -0.006466649 TRUE
## 2743 1 -0.4027408 1.591170453 TRUE
## 2744 -1 -0.4027408 1.502134162 TRUE
## 2745 1 -0.4027408 -0.341293263 FALSE
## 2746 -1 -0.4027408 -0.869240845 FALSE
## 2747 1 -0.4027408 1.478307549 TRUE
## 2748 -1 -0.4027408 -1.999123912 FALSE
## 2749 -1 -0.4027408 -1.200305362 FALSE
## 2750 1 -0.4027408 0.194178513 FALSE
## 2751 -1 -0.4027408 -1.950216654 FALSE
## 2752 -1 -0.4027408 -1.513813427 FALSE
## 2753 -1 -0.4027408 -1.190273104 FALSE
## 2754 -1 -0.4027408 -1.505035201 FALSE
## 2755 -1 -0.4027408 -1.887515041 FALSE
## 2756 1 -0.4027408 0.350932546 FALSE
## 2757 -1 -0.4027408 -1.972789235 FALSE
## 2758 -1 -0.4027408 -1.970281170 FALSE
## 2759 2 -0.4027408 1.691493033 TRUE
## 2760 -1 -0.4027408 0.049964803 TRUE
## 2761 -1 -0.4027408 -1.726998912 FALSE
## 2762 1 -0.4027408 0.811162386 FALSE
## 2763 -1 -0.4027408 -0.121837617 TRUE
## 2764 -1 -0.4027408 -0.987119877 FALSE
## 2765 3 -0.4027408 1.579884162 TRUE
## 2766 -1 -0.4027408 -1.926390041 FALSE
## 2767 -1 -0.4027408 -1.173970684 FALSE
## 2768 -1 -0.4027408 -0.886797296 FALSE
## 2769 -1 -0.4027408 -1.849894073 FALSE
## 2770 -1 -0.4027408 -1.072394071 FALSE
## 2771 -1 -0.4027408 0.318327707 TRUE
## 2772 -1 -0.4027408 -0.242224714 TRUE
## 2773 -1 -0.4027408 -1.103744878 FALSE
## 2774 3 -0.4027408 1.415605936 TRUE
## 2775 -1 -0.4027408 -0.203349714 TRUE
## 2776 -1 -0.4027408 -1.816035202 FALSE
## 2777 -1 -0.4027408 -1.468668266 FALSE
## 2778 -1 -0.4027408 -0.999660200 FALSE
## 2779 -1 -0.4027408 -1.885006977 FALSE
## 2780 -1 -0.4027408 -1.141365845 FALSE
## 2781 -1 -0.4027408 -1.310660201 FALSE
## 2782 -1 -0.4027408 -1.946454557 FALSE
## 2783 -1 -0.4027408 -1.561466653 FALSE
## 2784 -1 -0.4027408 -1.883752944 FALSE
## 2785 -1 -0.4027408 -1.123809394 FALSE
## 2786 -1 -0.4027408 -1.768381976 FALSE
## 2787 3 -0.4027408 -0.268559391 FALSE
## 2788 -1 -0.4027408 -1.022232781 FALSE
## 2789 -1 -0.4027408 -1.084934394 FALSE
## 2790 -1 -0.4027408 -1.439825524 FALSE
## 2791 -1 -0.4027408 -0.388946488 TRUE
## 2792 -1 -0.4027408 -1.812273105 FALSE
## 2793 -1 -0.4027408 -1.974043267 FALSE
## 2794 -1 -0.4027408 -1.221623910 FALSE
## 2795 -1 -0.4027408 -1.750825525 FALSE
## 2796 -1 -0.4027408 -0.565765037 TRUE
## 2797 -1 -0.4027408 -1.900055364 FALSE
## 2798 -1 -0.4027408 1.270138194 TRUE
## 2799 -1 -0.4027408 -2.002886009 FALSE
## 2800 3 -0.4027408 -0.192063423 FALSE
## 2801 -1 -0.4027408 -1.133841652 FALSE
## 2802 1 -0.4027408 0.133984965 FALSE
## 2803 -1 -0.4027408 -0.431583585 TRUE
## 2804 -1 -0.4027408 -0.619688425 TRUE
## 2805 -1 -0.4027408 -1.632946492 FALSE
## 2806 -1 -0.4027408 -1.325708588 FALSE
## 2807 -1 -0.4027408 -1.478700524 FALSE
## 2808 -1 -0.4027408 -1.764619879 FALSE
## 2809 -1 -0.4027408 -1.387156169 FALSE
## 2810 -1 -0.4027408 -1.037281168 FALSE
## 2811 -1 -0.4027408 -1.619152137 FALSE
## 2812 -1 -0.4027408 -0.579559392 TRUE
## 2813 3 -0.4027408 1.720335775 TRUE
## 2814 -1 -0.4027408 -1.640470686 FALSE
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## 2819 -1 -0.4027408 -1.827321493 FALSE
## 2820 -1 -0.4027408 -1.975297299 FALSE
## 2821 -1 -0.4027408 -1.738285202 FALSE
## 2822 3 -0.4027408 2.264585777 TRUE
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## 2824 -1 -0.4027408 -1.310660201 FALSE
## 2825 -1 -0.4027408 0.075045448 TRUE
## 2826 3 -0.4027408 1.344126097 TRUE
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## 2829 3 -0.4027408 1.949823679 TRUE
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## 2831 -1 -0.4027408 -1.649248911 FALSE
## 2832 -1 -0.4027408 -0.708724715 FALSE
## 2833 -1 -0.4027408 -1.896293267 FALSE
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## 2845 -1 -0.4027408 -0.545700521 TRUE
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## 2847 -1 -0.4027408 -1.916357783 FALSE
## 2848 3 -0.4027408 1.104605935 TRUE
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## 2850 3 -0.4027408 0.337138191 FALSE
## 2851 -1 -0.4027408 -1.898801331 FALSE
## 2852 -1 -0.4027408 -1.118793265 FALSE
## 2853 -1 -0.4027408 -1.681853750 FALSE
## 2854 3 -0.4027408 1.148497064 TRUE
## 2855 -1 -0.4027408 -1.827321493 FALSE
## 2856 -1 -0.4027408 -1.493748911 FALSE
## 2857 3 -0.4027408 0.833734967 FALSE
## 2858 2 -0.4027408 0.432444643 FALSE
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## 2863 -1 -0.4027408 -1.637962621 FALSE
## 2864 2 -0.4027408 1.468275291 TRUE
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## 2866 -1 -0.4027408 -1.298119878 FALSE
## 2867 -1 -0.4027408 -0.504317457 TRUE
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## 2871 -1 -0.4027408 -0.326244875 TRUE
## 2872 -1 -0.4027408 -0.317466650 TRUE
## 2873 -1 -0.4027408 -1.931406170 FALSE
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## 2875 -1 -0.4027408 -0.047849713 TRUE
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## 2877 -1 -0.4027408 -0.555732779 TRUE
## 2878 -1 -0.4027408 -1.324454556 FALSE
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## 2880 -1 -0.4027408 0.101380126 TRUE
## 2881 -1 -0.4027408 -1.946454557 FALSE
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## 2883 -1 -0.4027408 -1.482462621 FALSE
## 2884 -1 -0.4027408 0.901452709 TRUE
## 2885 -1 -0.4027408 -0.370136005 TRUE
## 2886 -1 -0.4027408 -1.145127942 FALSE
## 2887 -1 -0.4027408 -0.582067457 TRUE
## 2888 -1 -0.4027408 1.241295452 TRUE
## 2889 -1 -0.4027408 -0.316212617 TRUE
## 2890 -1 -0.4027408 0.472573675 TRUE
## 2891 3 -0.4027408 1.597440614 TRUE
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## 2893 1 -0.4027408 0.705823676 FALSE
## 2894 -1 -0.4027408 -1.946454557 FALSE
## 2895 -1 -0.4027408 -1.733269073 FALSE
## 2896 -1 -0.4027408 -0.619688425 TRUE
## 2897 -1 -0.4027408 -1.468668266 FALSE
## 2898 -1 -0.4027408 1.251327710 TRUE
## 2899 -1 -0.4027408 -1.094966652 FALSE
## 2900 1 -0.4027408 1.720335775 TRUE
## 2901 2 -0.4027408 1.118400290 TRUE
## 2902 -1 -0.4027408 -0.129361810 TRUE
## 2903 -1 -0.4027408 -1.200305362 FALSE
## 2904 -1 -0.4027408 -1.681853750 FALSE
## 2905 -1 -0.4027408 -0.271067456 TRUE
## 2906 1 -0.4027408 0.532767224 FALSE
## 2907 -1 -0.4027408 -0.440361811 TRUE
## 2908 -1 -0.4027408 -0.694930360 FALSE
## 2909 -1 -0.4027408 0.989234967 TRUE
## 2910 -1 -0.4027408 -1.333232781 FALSE
## 2911 -1 -0.4027408 -1.457381975 FALSE
## 2912 3 -0.4027408 0.280706739 FALSE
## 2913 -1 -0.4027408 -0.567019070 TRUE
## 2914 -1 -0.4027408 -1.178986813 FALSE
## 2915 -1 -0.4027408 -0.410265037 TRUE
## 2916 -1 -0.4027408 -1.251720684 FALSE
## 2917 1 -0.4027408 1.198658355 TRUE
## 2918 1 -0.4027408 1.238787387 TRUE
## 2919 -1 -0.4027408 -0.898083587 FALSE
## 2920 -1 -0.4027408 -0.238462617 TRUE
## 2921 -1 -0.4027408 -1.338248911 FALSE
## 2922 -1 -0.4027408 -0.615926328 TRUE
## 2923 -1 -0.4027408 -0.066660197 TRUE
## 2924 -1 -0.4027408 -2.058063429 FALSE
## 2925 -1 -0.4027408 -1.254228749 FALSE
## 2926 -1 -0.4027408 -0.658563425 TRUE
## 2927 -1 -0.4027408 -0.337531166 TRUE
## 2928 2 -0.4027408 0.393569643 FALSE
## 2929 -1 -0.4027408 -0.269813424 TRUE
## 2930 -1 -0.4027408 0.036170448 TRUE
## 2931 -1 -0.4027408 -0.916894071 FALSE
## 2932 1 -0.4027408 0.360964804 FALSE
## 2933 3 -0.4027408 0.088839803 FALSE
## 2934 -1 -0.4027408 -2.009156170 FALSE
## 2935 -1 -0.4027408 -0.488015037 TRUE
## 2936 -1 -0.4027408 -1.856164235 FALSE
## 2937 -1 -0.4027408 -1.224131975 FALSE
## 2938 -1 -0.4027408 -0.283607779 TRUE
## 2939 -1 -0.4027408 -0.646023102 TRUE
## 2940 3 -0.4027408 1.528468839 TRUE
## 2941 -1 -0.4027408 0.488876095 TRUE
## 2942 -1 -0.4027408 -0.094248907 TRUE
## 2943 1 -0.4027408 0.281960771 FALSE
## 2944 -1 -0.4027408 -0.277337617 TRUE
## 2945 -1 -0.4027408 -1.113777136 FALSE
## 2946 -1 -0.4027408 -0.302418262 TRUE
## 2947 -1 -0.4027408 0.472573675 TRUE
## 2948 -1 -0.4027408 -0.158204552 TRUE
## 2949 -1 -0.4027408 -1.807256976 FALSE
## 2950 3 -0.4027408 1.209944645 TRUE
## 2951 3 -0.4027408 1.487085775 TRUE
## 2952 -1 -0.4027408 -0.407756972 TRUE
## 2953 -1 -0.4027408 -0.941974716 FALSE
## 2954 -1 -0.4027408 -0.979595684 FALSE
## 2955 3 -0.4027408 2.110339809 TRUE
## 2956 -1 -0.4027408 0.986726902 TRUE
## 2957 -1 -0.4027408 -1.252974717 FALSE
## 2958 -1 -0.4027408 -1.739539234 FALSE
## 2959 -1 -0.4027408 0.434952707 TRUE
## 2960 -1 -0.4027408 0.053726900 TRUE
## 2961 3 -0.4027408 1.441940613 TRUE
## 2962 -1 -0.4027408 -0.251002940 TRUE
## 2963 -1 -0.4027408 -1.928898106 FALSE
## 2964 1 -0.4027408 1.836960776 TRUE
## 2965 -1 -0.4027408 -0.773934393 FALSE
## 2966 -1 -0.4027408 -0.328752940 TRUE
## 2967 -1 -0.4027408 -0.629720683 TRUE
## 2968 -1 -0.4027408 0.388553514 TRUE
## 2969 -1 -0.4027408 -1.589055363 FALSE
## 2970 -1 -0.4027408 -0.751361812 FALSE
## 2971 1 -0.4027408 0.829972870 FALSE
## 2972 -1 -0.4027408 -0.376406166 TRUE
## 2973 1 -0.4027408 -0.213381972 FALSE
## 2974 -1 -0.4027408 -1.092458587 FALSE
## 2975 1 -0.4027408 1.591170453 TRUE
## 2976 -1 -0.4027408 1.148497064 TRUE
## 2977 3 -0.4027408 0.190416416 FALSE
## 2978 2 -0.4027408 0.620549482 FALSE
## 2979 -1 -0.4027408 -0.609656167 TRUE
## 2980 3 -0.4027408 0.735920450 FALSE
## 2981 1 -0.4027408 0.748460773 FALSE
## 2982 2 -0.4027408 0.962900289 TRUE
## 2983 3 -0.4027408 0.732158353 FALSE
## 2984 -1 -0.4027408 -0.672357780 FALSE
## 2985 -1 -0.4027408 0.164081739 TRUE
## 2986 2 -0.4027408 0.394823675 FALSE
## 2987 1 -0.4027408 0.249355933 FALSE
## 2988 3 -0.4027408 0.605501095 FALSE
## 2989 -1 -0.4027408 0.266912384 TRUE
## 2990 1 -0.4027408 0.329613997 FALSE
## 2991 -1 -0.4027408 0.860069644 TRUE
## 2992 -1 -0.4027408 -0.598369876 TRUE
## 2993 -1 -0.4027408 -1.513813427 FALSE
## 2994 -1 -0.4027408 -1.357059394 FALSE
## 2995 -1 -0.4027408 0.841259160 TRUE
## 2996 -1 -0.4027408 0.606755127 TRUE
## 2997 -1 -0.4027408 -1.056091652 FALSE
## 2998 -1 -0.4027408 -1.841115847 FALSE
## 2999 -1 -0.4027408 -0.140648101 TRUE
## 3000 -1 -0.4027408 -0.019006971 TRUE
## 3001 -1 -0.4027408 0.224275287 TRUE
## 3002 -1 -0.4027408 0.149033352 TRUE
## 3003 -1 -0.4027408 -0.812809393 FALSE
## 3004 -1 -0.4027408 -0.225922294 TRUE
## 3005 1 -0.4027408 2.214424487 TRUE
## 3006 -1 -0.4027408 -1.701918266 FALSE
## 3007 -1 -0.4027408 -0.368881972 TRUE
## 3008 -1 -0.4027408 -0.972071490 FALSE
## 3009 -1 -0.4027408 0.047456739 TRUE
## 3010 -1 -0.4027408 0.026138190 TRUE
## 3011 -1 -0.4027408 -1.190273104 FALSE
## 3012 -1 -0.4027408 -0.189555359 TRUE
## 3013 3 -0.4027408 0.537783353 FALSE
## 3014 -1 -0.4027408 -1.877482783 FALSE
##
## $meta
## $meta$x
## [1] "Hydra_k3"
##
## $meta$y
## [1] "overall_psychopathology_4factorv2"
##
## $meta$hasInteraction
## [1] FALSE
##
## $meta$yName
## [1] "overall_psychopathology_4factorv2"
##
## $meta$trans
## function (x)
## {
## structure(x, class = unique(c("AsIs", oldClass(x))))
## }
## <bytecode: 0x7fe75e9bc710>
## <environment: namespace:base>
##
## $meta$class
## [1] "lm"
##
##
## attr(,"class")
## [1] "visreg"#WILL HAVE TO DO THIS FOR MALES AND FEMALES#
#######Chi-square for males/females and race########
##########By males, and by caucasians ##############
#Chi squared sex - significance p = 0.12
#if just want to look at clusters, not TD, look at commented line below, otherwise keep as is
###chisq_matched_sex <- chisq.test(subset_with_clusters_AG_matched$sex, (subset_with_clusters_AG_matched$Hydra_k3 != -1))
chisq_matched_sex <- chisq.test(subset_with_clusters_AG_matched$sex, subset_with_clusters_AG_matched$Hydra_k3)
chisq_matched_sex_pvalue <- chisq_matched_sex$p.value
total_people_Hydra_k3 <- c(length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == -1)]),
length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == 1)]),
length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == 2)]),
length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == 3)]))
num_men_all_clusters <- c(length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == -1 & subset_with_clusters_AG_matched$sex == 1)]),
length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == 1 & subset_with_clusters_AG_matched$sex == 1)]),
length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == 2 & subset_with_clusters_AG_matched$sex == 1)]),
length(subset_with_clusters_AG_matched$sex[which(subset_with_clusters_AG_matched$Hydra_k3 == 3 & subset_with_clusters_AG_matched$sex == 1)]))
percent_men_all_clusters <- (num_men_all_clusters/total_people_Hydra_k3) * 100
#Chi square race - p-value = 4.854e-13
chisq_matched_race <- chisq.test(subset_with_clusters_AG_matched$race_binarized, subset_with_clusters_AG_matched$Hydra_k3)
chisq_matched_race_pvalue <- chisq_matched_race$p.value
num_caucasian_all_clusters <- c(length(subset_with_clusters_AG_matched$race_binarized[which(subset_with_clusters_AG_matched$Hydra_k3 == -1 & subset_with_clusters_AG_matched$race_binarized == 1)]),
length(subset_with_clusters_AG_matched$race_binarized[which(subset_with_clusters_AG_matched$Hydra_k3 == 1 & subset_with_clusters_AG_matched$race_binarized == 1)]),
length(subset_with_clusters_AG_matched$race_binarized[which(subset_with_clusters_AG_matched$Hydra_k3 == 2 & subset_with_clusters_AG_matched$race_binarized == 1)]),
length(subset_with_clusters_AG_matched$race_binarized[which(subset_with_clusters_AG_matched$Hydra_k3 == 3 & subset_with_clusters_AG_matched$race_binarized == 1)]))
percent_caucasian_all_clusters <- (num_caucasian_all_clusters/total_people_Hydra_k3)*100
p_values <- c("", chisq_matched_sex_pvalue, "", chisq_matched_race_pvalue)
dat_sex_race <- data.frame(cl = c("TD", "Cluster1", "Cluster2", "Cluster3", "Significance"),
num_males = c(num_men_all_clusters, "---"),
percent_males = round(c(percent_men_all_clusters, chisq_matched_sex_pvalue), 2),
num_caucasians = c(num_caucasian_all_clusters, "---"),
percent_caucasian = round(c(percent_caucasian_all_clusters, chisq_matched_race_pvalue),2))
dat_sex_race_no_significance <- data.frame(cl = c("TD", "Cluster1", "Cluster2", "Cluster3"),
num_males = (num_men_all_clusters),
percent_males = percent_men_all_clusters,
num_caucasians = num_caucasian_all_clusters,
percent_caucasian = percent_caucasian_all_clusters)
ggplot(dat_sex_race_no_significance, aes(x = cl, y = percent_males, fill=cl)) + geom_col() +
scale_x_discrete(limits=c("TD", "Cluster1", "Cluster2", "Cluster3")) + ylim(0, 100) + xlab("Clusters") + ylab("% Male") +
ggtitle("Hydra_k3 % Male, p = 0.122") + scale_fill_discrete(breaks=c("TD", "Cluster1", "Cluster2", "Cluster3")) +
guides(fill=guide_legend(title=NULL))
ggplot(dat_sex_race_no_significance, aes(x = cl, y = percent_caucasian, fill=cl)) + geom_col() +
scale_x_discrete(limits=c("TD", "Cluster1", "Cluster2", "Cluster3")) + ylim(0, 100) + xlab("Clusters") + ylab("% Caucasian") +
ggtitle("Hydra_k3 % Caucasian, p = 4.854e-13") + scale_fill_discrete(breaks=c("TD", "Cluster1", "Cluster2", "Cluster3")) +
guides(fill=guide_legend(title=NULL))
#############################
######### Bar Graphs ########
#############################
dat <- data.frame(cluster=c(subset_with_clusters_AG_matched$Hydra_k3), age=c(subset_with_clusters_AG_matched$age_in_years), medu1=c(subset_with_clusters_AG_matched$medu1), race=c(subset_with_clusters_AG_matched$race_binarized), sex_males=c(subset_with_clusters_AG_matched$sex))
#dat_age_sd_sem is the data frame that will be cluster(vertical), columns (age, medu), last rows are standard dec and sem
dat_age_sd_sem <- data.frame(cl = c("TD", "Cluster1", "Cluster2", "Cluster3"), age = c(mean(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==-1)]), mean(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==1)]), mean(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==2)]), mean(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==3)])), age_sd = c(sd(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==-1)]), sd(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==1)]), sd(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==2)]), sd(subset_with_clusters_AG_matched$age_in_years[which(subset_with_clusters_AG_matched$Hydra_k3 ==3)])))
dat_age_sd_sem$sem <- dat_age_sd_sem$age_sd/sqrt(nrow(dat))
dat_medu_sd_sem <- data.frame(cl = c("TD", "Cluster1", "Cluster2", "Cluster3"), medu = c(mean(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==-1)]), mean(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==1)]), mean(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==2)]), mean(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==3)])), age_sd = c(sd(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==-1)]), sd(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==1)]), sd(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==2)]), sd(subset_with_clusters_AG_matched$medu1[which(subset_with_clusters_AG_matched$Hydra_k3 ==3)])))
dat_medu_sd_sem$sem <- dat_medu_sd_sem$age_sd/sqrt(nrow(dat))
dat_cont <- data.frame(cluster=c(subset_with_clusters_AG_matched$Hydra_k3), age=c(subset_with_clusters_AG_matched$age_in_years), medu1=c(subset_with_clusters_AG_matched$medu1))
dat_cat <- data.frame(cluster=c(subset_with_clusters_AG_matched$Hydra_k3), race=c(subset_with_clusters_AG_matched$race_binarized), sex=c(subset_with_clusters_AG_matched$sex))
dat.m <- melt(dat, id.vars='cluster')
dat_cont.m <- melt(dat_cont, id.vars='cluster')
dat_cat.m <- melt(dat_cat, id.vars='cluster')
#ggplot(dat.m, aes(fill=cluster, x=cluster, y=value))+ geom_bar(stat="identity") + facet_grid(.~variable) +
# labs(x='Clusters_matched',y='') + scale_x_discrete(limits=c("TD", "Cluster1", "Cluster2", "Cluster3"))
ggplot(dat_age_sd_sem, aes(x = cl, y = age, fill = cl)) + geom_col() +
geom_errorbar(aes(ymin=age-sem, ymax=age+sem),width=.2,position=position_dodge(.9)) +
scale_x_discrete(limits=c("TD", "Cluster1", "Cluster2", "Cluster3")) + ylim(0, 18) + xlab("Clusters") + ylab("Age in Years") +
ggtitle("Age by Cluster") + scale_fill_discrete(breaks=c("TD", "Cluster1", "Cluster2", "Cluster3")) +
guides(fill=guide_legend(title=NULL)) 
ggplot(dat_medu_sd_sem, aes(x = cl, y = medu, fill = cl)) + geom_col() +
geom_errorbar(aes(ymin=medu-sem, ymax=medu+sem),width=.2,position=position_dodge(.9)) +
scale_x_discrete(limits=c("TD", "Cluster1", "Cluster2", "Cluster3")) + ylim(0, 18) + xlab("Clusters") + ylab("Maternal Ed in Years") +
ggtitle("Maternal Edu by Cluster") + scale_fill_discrete(breaks=c("TD", "Cluster1", "Cluster2", "Cluster3")) +
guides(fill=guide_legend(title=NULL)) 
#ggplot(dat_cont.m, aes(fill=cluster, x=cluster, y=value)) + geom_bar(stat='identity') +
# facet_grid(.~variable) + labs(x='Clusters_matched',y='') +
#scale_x_discrete(limits=c("TD", "Cluster1", "Cluster2", "Cluster3"))
#ggplot(dat_cat.m, aes(fill=cluster, x=cluster, y=value)) + geom_bar(stat='identity') +
# facet_grid(.~variable) + labs(x='Clusters_matched',y='') +
# scale_x_discrete(limits=c("TD", "Cluster1", "Cluster2", "Cluster3"))FDR correction
###################################################
## Extracting anovas with significant p values ####
###################################################
#Looking at Hydra_k3, but can redo with nested list
#Look at model summaries
models_anova <- lapply(clinical_cog_score_cluster_stats_anova_AG_matched, summary)
#Pull p-values
p_anova <- sapply(clinical_cog_score_cluster_stats_anova_AG_matched, function(v) v$"Pr(>F)"[1]) #$coef[,"Pr(>F)"][2]) #get the p value for dep binarized
#Convert to data frame
p_anova <- as.data.frame(p_anova)
#Print original p-values to three decimal places
p_round_anova <- round(p_anova,3)
#FDR correct p-values
pfdr_anova <- p.adjust(p_anova[,1],method="fdr")
#Convert to data frame
pfdr_anova <- as.data.frame(pfdr_anova)
row.names(pfdr_anova) <- clinical_measure_names
#To print fdr-corrected p-values to three decimal places
pfdr_round_anova <- round(pfdr_anova,3)
#List the NMF components that survive FDR correction
clinical_fdr_anova <- row.names(pfdr_anova)[pfdr_anova<0.05]
#make a data frame with names and fdr values (rounded to 3 decimals)
clinical_names_and_fdr_values_anova <- data.frame(cbind(clinical_fdr_anova, round(pfdr_anova[pfdr_anova<0.05],3)))
#add titles to names_and_fdr tables
names(clinical_names_and_fdr_values_anova) <- c("clinical_measure", "p_FDR_corr")
#write the results of the mass univariate stats to files
write.csv(clinical_names_and_fdr_values_anova, file = "/Users/eballer/BBL/from_chead/ballerDepHeterogen/results/hydra_k3_clinical_20180315.csv")
print(clinical_names_and_fdr_values_anova)## clinical_measure p_FDR_corr
## 1 mood_4factorv2 0
## 2 psychosis_4factorv2 0.007
## 3 externalizing_4factorv2 0
## 4 phobias_4factorv2 0
## 5 overall_psychopathology_4factorv2 0Demographics for matched, unmatched and unmatched_resid group by cluster
#############################
####### Demographics ########
#############################
#######Matched group all clusters ##############
#subset demographics
listVars <- c("Race", "Sex", "Maternal Ed", "Age", "Depression", "Cluster") #Race 1 = caucasian, Maternal Ed = years, age = years, dep 1 = dep, 0 = non_dep
demo_hydra_k3_AG_matched <- data.frame(subset_with_clusters_AG_matched$race_binarized, subset_with_clusters_AG_matched$sex, subset_with_clusters_AG_matched$medu1, subset_with_clusters_AG_matched$age_in_years, subset_with_clusters_AG_matched$dep_binarized, subset_with_clusters_AG_matched$Hydra_k3)
names(demo_hydra_k3_AG_matched) <- c(listVars)
#Change categorical values to have names
demo_hydra_k3_AG_matched$Depression <- ifelse(demo_hydra_k3_AG_matched$Depression == 1, "Depressed", "Non-depressed")
demo_hydra_k3_AG_matched$Race <- ifelse(demo_hydra_k3_AG_matched$Race == 1, "Caucasian", "Non-caucasian")
demo_hydra_k3_AG_matched$Sex <- ifelse(demo_hydra_k3_AG_matched$Sex == 1, "Male", "Female")
#Define Categorical Variables
cat_variables <- c("Race", "Depression", "Sex", "Cluster")
title <- c("hydra_k3 demographics")
#create demographics table
demo_hydra_k3_AG_matched_table <- CreateTableOne(vars = listVars, data = demo_hydra_k3_AG_matched, factorVars = cat_variables, strata = c("Cluster"))
print(demo_hydra_k3_AG_matched_table, showAllLevels = TRUE)## Stratified by Cluster
## level -1 1
## n 711 262
## Race (%) Caucasian 393 ( 55.3) 97 ( 37.0)
## Non-caucasian 318 ( 44.7) 165 ( 63.0)
## Sex (%) Female 476 ( 66.9) 179 ( 68.3)
## Male 235 ( 33.1) 83 ( 31.7)
## Maternal Ed (mean (sd)) 14.14 (2.26) 13.56 (2.16)
## Age (mean (sd)) 16.11 (3.00) 15.30 (3.38)
## Depression (%) Depressed 0 ( 0.0) 262 (100.0)
## Non-depressed 711 (100.0) 0 ( 0.0)
## Cluster (%) -1 711 (100.0) 0 ( 0.0)
## 1 0 ( 0.0) 262 (100.0)
## 2 0 ( 0.0) 0 ( 0.0)
## 3 0 ( 0.0) 0 ( 0.0)
## Stratified by Cluster
## 2 3 p test
## n 262 188
## Race (%) 184 ( 70.2) 112 ( 59.6) <0.001
## 78 ( 29.8) 76 ( 40.4)
## Sex (%) 162 ( 61.8) 136 ( 72.3) 0.122
## 100 ( 38.2) 52 ( 27.7)
## Maternal Ed (mean (sd)) 14.55 (2.32) 14.31 (2.28) <0.001
## Age (mean (sd)) 16.84 (2.40) 16.31 (2.49) <0.001
## Depression (%) 262 (100.0) 188 (100.0) <0.001
## 0 ( 0.0) 0 ( 0.0)
## Cluster (%) 0 ( 0.0) 0 ( 0.0) <0.001
## 0 ( 0.0) 0 ( 0.0)
## 262 (100.0) 0 ( 0.0)
## 0 ( 0.0) 188 (100.0)#######Unmatched group ##############
#subset demographics
listVars <- c("Race", "Sex", "Maternal Ed", "Age", "Depression", "Cluster") #Race 1 = caucasian, Maternal Ed = years, age = years, dep 1 = dep, 0 = non_dep
demo_hydra_k3_AG_unmatched <- data.frame(subset_with_clusters_AG_unmatched$race_binarized, subset_with_clusters_AG_unmatched$sex, subset_with_clusters_AG_unmatched$medu1, subset_with_clusters_AG_unmatched$age_in_years, subset_with_clusters_AG_unmatched$dep_binarized, subset_with_clusters_AG_unmatched$Hydra_k3)
names(demo_hydra_k3_AG_unmatched) <- c(listVars)
#Change categorical values to have names
demo_hydra_k3_AG_unmatched$Depression <- ifelse(demo_hydra_k3_AG_unmatched$Depression == 1, "Depressed", "Non-depressed")
demo_hydra_k3_AG_unmatched$Race <- ifelse(demo_hydra_k3_AG_unmatched$Race == 1, "Caucasian", "Non-caucasian")
demo_hydra_k3_AG_unmatched$Sex <- ifelse(demo_hydra_k3_AG_unmatched$Sex == 1, "Male", "Female")
#Define Categorical Variables
cat_variables <- c("Race", "Depression", "Sex", "Cluster")
title <- c("hydra_k3 demographics")
#create demographics table
demo_hydra_k3_AG_unmatched_table <- CreateTableOne(vars = listVars, data = demo_hydra_k3_AG_unmatched, factorVars = cat_variables, strata = c("Cluster"))
print(demo_hydra_k3_AG_unmatched_table, showAllLevels = TRUE)## Stratified by Cluster
## level -1 1
## n 2297 268
## Race (%) Caucasian 1469 ( 64.0) 103 ( 38.4)
## Non-caucasian 828 ( 36.0) 165 ( 61.6)
## Sex (%) Female 1103 ( 48.0) 180 ( 67.2)
## Male 1194 ( 52.0) 88 ( 32.8)
## Maternal Ed (mean (sd)) 14.93 (2.45) 13.65 (2.26)
## Age (mean (sd)) 13.83 (3.72) 16.27 (3.04)
## Depression (%) Depressed 0 ( 0.0) 268 (100.0)
## Non-depressed 2297 (100.0) 0 ( 0.0)
## Cluster (%) -1 2297 (100.0) 0 ( 0.0)
## 1 0 ( 0.0) 268 (100.0)
## 2 0 ( 0.0) 0 ( 0.0)
## 3 0 ( 0.0) 0 ( 0.0)
## Stratified by Cluster
## 2 3 p test
## n 211 238
## Race (%) 136 ( 64.5) 156 ( 65.5) <0.001
## 75 ( 35.5) 82 ( 34.5)
## Sex (%) 150 ( 71.1) 151 ( 63.4) <0.001
## 61 ( 28.9) 87 ( 36.6)
## Maternal Ed (mean (sd)) 14.36 (2.13) 14.43 (2.37) <0.001
## Age (mean (sd)) 15.94 (2.77) 16.11 (2.84) <0.001
## Depression (%) 211 (100.0) 238 (100.0) <0.001
## 0 ( 0.0) 0 ( 0.0)
## Cluster (%) 0 ( 0.0) 0 ( 0.0) <0.001
## 0 ( 0.0) 0 ( 0.0)
## 211 (100.0) 0 ( 0.0)
## 0 ( 0.0) 238 (100.0)#######Ununmatched residuals group ##############
#subset demographics
listVars <- c("Race", "Sex", "Maternal Ed", "Age", "Depression", "Cluster") #Race 1 = caucasian, Maternal Ed = years, age = years, dep 1 = dep, 0 = non_dep
demo_hydra_k3_AG_resid <- data.frame(subset_with_clusters_AG_resid$race_binarized, subset_with_clusters_AG_resid$sex, subset_with_clusters_AG_resid$medu1, subset_with_clusters_AG_resid$age_in_years, subset_with_clusters_AG_resid$dep_binarized, subset_with_clusters_AG_resid$Hydra_k3)
names(demo_hydra_k3_AG_resid) <- c(listVars)
#Change categorical values to have names
demo_hydra_k3_AG_resid$Depression <- ifelse(demo_hydra_k3_AG_resid$Depression == 1, "Depressed", "Non-depressed")
demo_hydra_k3_AG_resid$Race <- ifelse(demo_hydra_k3_AG_resid$Race == 1, "Caucasian", "Non-caucasian")
demo_hydra_k3_AG_resid$Sex <- ifelse(demo_hydra_k3_AG_resid$Sex == 1, "Male", "Female")
#make variable list
#table_titles <- c("Cluster 1", "Cluster 2")
#table_titles <- c("Cluster 1", "Depressed", "P-value")
#Define Categorical Variables
cat_variables <- c("Race", "Depression", "Sex", "Cluster")
title <- c("Hydra_k3 demographics")
#create demographics table
demo_hydra_k3_AG_resid_table <- CreateTableOne(vars = listVars, data = demo_hydra_k3_AG_resid, factorVars = cat_variables, strata = c("Cluster"))
print(demo_hydra_k3_AG_resid_table, showAllLevels = TRUE)## Stratified by Cluster
## level -1 1
## n 2297 251
## Race (%) Caucasian 1469 ( 64.0) 146 ( 58.2)
## Non-caucasian 828 ( 36.0) 105 ( 41.8)
## Sex (%) Female 1103 ( 48.0) 177 ( 70.5)
## Male 1194 ( 52.0) 74 ( 29.5)
## Maternal Ed (mean (sd)) 14.93 (2.45) 14.20 (2.22)
## Age (mean (sd)) 13.83 (3.72) 16.17 (2.74)
## Depression (%) Depressed 0 ( 0.0) 251 (100.0)
## Non-depressed 2297 (100.0) 0 ( 0.0)
## Cluster (%) -1 2297 (100.0) 0 ( 0.0)
## 1 0 ( 0.0) 251 (100.0)
## 2 0 ( 0.0) 0 ( 0.0)
## 3 0 ( 0.0) 0 ( 0.0)
## Stratified by Cluster
## 2 3 p test
## n 226 240
## Race (%) 152 ( 67.3) 97 ( 40.4) <0.001
## 74 ( 32.7) 143 ( 59.6)
## Sex (%) 139 ( 61.5) 165 ( 68.8) <0.001
## 87 ( 38.5) 75 ( 31.2)
## Maternal Ed (mean (sd)) 14.46 (2.32) 13.71 (2.28) <0.001
## Age (mean (sd)) 16.27 (2.95) 15.93 (3.00) <0.001
## Depression (%) 226 (100.0) 240 (100.0) <0.001
## 0 ( 0.0) 0 ( 0.0)
## Cluster (%) 0 ( 0.0) 0 ( 0.0) <0.001
## 0 ( 0.0) 0 ( 0.0)
## 226 (100.0) 0 ( 0.0)
## 0 ( 0.0) 240 (100.0)Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.